{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 1 Introduction \n",
    "* 2 Load and check data\n",
    "    * 2.1 load data \n",
    "    * 2.2 Outlier detectio \n",
    "    * 2.3 join train and test set \n",
    "    * check for null and missing values \n",
    "* 3 Feature analysis \n",
    "    * 3.1 Numerical values \n",
    "    * 3.2 Categorical values \n",
    "* 4 Filling missing Values \n",
    "    * 4.1 Age \n",
    "* 5 Feature engineering \n",
    "    * 5.1 Name/Title \n",
    "    * 5.2 Family Size \n",
    "    * Cabin \n",
    "    * Ticket \n",
    "* Modeling \n",
    "    * 6.1 Simple modeling \n",
    "        * 6.1.1 Cross validate models \n",
    "        * 6.1.2 Hyperparmater tunning for best models \n",
    "        * 6.1.3 Plot learning curves \n",
    "        * 6.1.4 Feature importance of the tree based classifiers \n",
    "    * 6.2 Ensemble modeling \n",
    "        * 6.2.1 Combining models \n",
    "    * Prediction \n",
    "        * Predict and Submit results "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  1. Introduction \n",
    "## This script follows three main parts \n",
    "* Feature analysis \n",
    "* Feature engineering \n",
    "* Modeling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "pd.options.display.max_columns = 50\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "%matplotlib inline \n",
    "from collections import Counter \n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier,\\\n",
    "GradientBoostingClassifier, ExtraTreesClassifier, VotingClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.linear_model import LogisticRegression \n",
    "from sklearn.neighbors import KNeighborsClassifier \n",
    "from sklearn.tree import DecisionTreeClassifier \n",
    "from sklearn.neural_network import MLPClassifier \n",
    "from sklearn.svm import SVC \n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score, StratifiedKFold, learning_curve "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.Load and check data\n",
    "### 2.1 Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('titanic_train.csv')\n",
    "test = pd.read_csv('titanic_test.csv')\n",
    "IDtest = test['PassengerId']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Outlier detection "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[27, 88, 159, 180, 201, 324, 341, 792, 846, 863]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def detect_outliers(df, n, features):\n",
    "    outlier_indices = []\n",
    "    for col in features:\n",
    "        Q1 = np.percentile(df[col], 25)\n",
    "        Q3 = np.percentile(df[col], 75)\n",
    "        IQR = Q3 - Q1 \n",
    "        outlier_step = 1.5 * IQR \n",
    "        outlier_list_col = df[(df[col] < Q1 - outlier_step) | (df[col] > Q3 + outlier_step)].index\n",
    "        outlier_indices.extend(outlier_list_col)\n",
    "    outlier_indices = Counter(outlier_indices)\n",
    "    multiple_outliers = list(k for k, v in outlier_indices.items() if v > n)\n",
    "    return multiple_outliers \n",
    "\n",
    "Outliers_to_drop = detect_outliers(train, 2, [ 'SibSp', 'Parch', 'Fare'])\n",
    "Outliers_to_drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.drop(Outliers_to_drop, axis=0).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 joining train and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=True'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass sort=False\n",
      "\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "train_len = len(train)\n",
    "dataset = pd.concat(objs=[train, test], axis=0).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 check for null and missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age             256\n",
       "Cabin          1007\n",
       "Embarked          2\n",
       "Fare              1\n",
       "Name              0\n",
       "Parch             0\n",
       "PassengerId       0\n",
       "Pclass            0\n",
       "Sex               0\n",
       "SibSp             0\n",
       "Survived        418\n",
       "Ticket            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill empty and NaNs values with NaN\n",
    "dataset.fillna(np.nan)\n",
    "# check for Null values \n",
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 881 entries, 0 to 880\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    881 non-null int64\n",
      "Survived       881 non-null int64\n",
      "Pclass         881 non-null int64\n",
      "Name           881 non-null object\n",
      "Sex            881 non-null object\n",
      "Age            711 non-null float64\n",
      "SibSp          881 non-null int64\n",
      "Parch          881 non-null int64\n",
      "Ticket         881 non-null object\n",
      "Fare           881 non-null float64\n",
      "Cabin          201 non-null object\n",
      "Embarked       879 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 82.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            170\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          680\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>881.000000</td>\n",
       "      <td>881.000000</td>\n",
       "      <td>881.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>881.000000</td>\n",
       "      <td>881.000000</td>\n",
       "      <td>881.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.713961</td>\n",
       "      <td>0.385925</td>\n",
       "      <td>2.307605</td>\n",
       "      <td>29.731603</td>\n",
       "      <td>0.455165</td>\n",
       "      <td>0.363224</td>\n",
       "      <td>31.121566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>256.617021</td>\n",
       "      <td>0.487090</td>\n",
       "      <td>0.835055</td>\n",
       "      <td>14.547835</td>\n",
       "      <td>0.871571</td>\n",
       "      <td>0.791839</td>\n",
       "      <td>47.996249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>226.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.895800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>448.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   881.000000  881.000000  881.000000  711.000000  881.000000   \n",
       "mean    446.713961    0.385925    2.307605   29.731603    0.455165   \n",
       "std     256.617021    0.487090    0.835055   14.547835    0.871571   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     226.000000    0.000000    2.000000   20.250000    0.000000   \n",
       "50%     448.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.000000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    5.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  881.000000  881.000000  \n",
       "mean     0.363224   31.121566  \n",
       "std      0.791839   47.996249  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.895800  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   30.500000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summarize data \n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.Feature analysis\n",
    "### 3.1 Numerical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# correlation matrix between numerical values(SipSp Parch Age and Fare values) and Survived \n",
    "g = sns.heatmap(train[['Survived', 'SibSp', 'Parch', 'Age', 'Fare']].corr(), annot=True, fmt='.2f', cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only Fare feature seems to have a significative correlation with the survival probability \n",
    "\n",
    "It doesn't mean that the other feature are not useful.\n",
    "\n",
    "Subpopulations in these features can be correlated with the survival. \n",
    "\n",
    "We need to explore in detail these features "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SibSp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1d8a6b45c88>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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58PcMfnQrSVJvRlrNHPgI8DtVNdtKEJIkHTKzBlR3y4xvV9U756keSZKAOYb4uvtA3S/JkfNUjyRJwGhDfN8DvpRkE3D7OnxVdUFvVUmSJt4oAXV99zgMOKbfciRJGpgzoKrqLfNRiCRJw0a53cZlzHyjwSf2UpEkSYw2xPe6oe2jgN8H9vdTjiRJA6MM8V0x7dCXkoxyy3dJku6yUYb47ju0exjwKGBZbxVJksRoQ3xXMLgGFQZDe98BXtpnUZIkjTLE58qZkqR5N8pisc9Ocky3/cYkH0/yyP5LkyRNslFuWPimqropyeOBpwIXAe/utyxJ0qQbJaBu656fDry7qj4JuDafJKlXowTUniTvAc4ENie5x4jvkyTpLhslaM4EtgBrqupHwH2B1/dalSRp4o0yi++nwMeH9r8PfL/PoiRJcqhOktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktSkXgMqyZokO5PsSnLODK+/Jsk1Sa5O8ndJ/nWf9UiSFo7eAirJEuBC4GnAicBzk5w4rdnXgNVV9TDgEmB9X/VIkhaWPs+gTgZ2VdW1VXUrsBE4fbhBVV1WVT/tdr8MLO+xHknSAtJnQB0PXDe0v7s7djAvBf52pheSrE2yLcm2vXv3HsISJUmt6jOgMsOxmrFh8gJgNfC2mV6vqg1VtbqqVi9duvQQlihJatXhPfa9G1gxtL8cuH56oyRPBt4APKGqft5jPZKkBaTPM6itwKokK5McCZwFbBpukOQRwHuA06rqhh5rkSQtML0FVFXtB84GtgA7gIuranuS85Oc1jV7G3BP4GNJrkqy6SDdSZImTJ9DfFTVZmDztGPnDW0/uc/PlyQtXK4kIUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqUq+rmUvS3bVl42t77f+nN+27/bmvz3rqWW/vpd/FzjMoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSk5xmLi0Qf/3Hl/Xa/09+cMvtz31+1jP/06m99a3FxTMoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpN6Dagka5LsTLIryTkzvP7bSa5Msj/JGX3WIklaWHoLqCRLgAuBpwEnAs9NcuK0Zv8XeDHw4b7qkCQtTIf32PfJwK6quhYgyUbgdOCaAw2q6rvda7/ssQ5J0gLU5xDf8cB1Q/u7u2N3WpK1SbYl2bZ3795DUpwkqW19BlRmOFZ3paOq2lBVq6tq9dKlS+9mWZKkhaDPgNoNrBjaXw5c3+PnSZIWkT4DaiuwKsnKJEcCZwGbevw8SdIi0ltAVdV+4GxgC7ADuLiqtic5P8lpAEkenWQ38GzgPUm291WPJGlh6XMWH1W1Gdg87dh5Q9tbGQz9SZJ0B64kIUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWpSrwGVZE2SnUl2JTlnhtfvkeSj3etfSfKAPuuRJC0cvQVUkiXAhcDTgBOB5yY5cVqzlwI/rKpfB94B/Glf9UiSFpY+z6BOBnZV1bVVdSuwETh9WpvTgYu67UuAJyVJjzVJkhaIVFU/HSdnAGuq6mXd/guBx1TV2UNtvtm12d3tf7trs29aX2uBtd3uQ4CdvRQ9t/sD++ZstfhM4veexO8Mfu9JMs7vvK+q1szV6PAeC5jpTGh6Go7ShqraAGw4FEXdHUm2VdXqcdcx3ybxe0/idwa/97jrmE8L4Tv3OcS3G1gxtL8cuP5gbZIcDtwb+EGPNUmSFog+A2orsCrJyiRHAmcBm6a12QS8qNs+A/h89TXmKElaUHob4quq/UnOBrYAS4APVNX2JOcD26pqE/B+4INJdjE4czqrr3oOkbEPM47JJH7vSfzO4PeeJM1/594mSUiSdHe4koQkqUkGlCSpSQbUCOZasmmxSvKBJDd0v1ebCElWJLksyY4k25O8etw1zYckRyX5apKvd9/7LeOuab4kWZLka0n+Zty1zJck303yjSRXJdk27noOxmtQc+iWbPon4CkMpsVvBZ5bVdeMtbB5kOS3gZuBv6yqk8Zdz3xIchxwXFVdmeQY4ArgmYv9v3e3gsvRVXVzkiOALwKvrqovj7m03iV5DbAauFdVPWPc9cyHJN8FVk9fFKE1nkHNbZQlmxalqvrfTNjv0qrq+1V1Zbd9E7ADOH68VfWvBm7udo/oHov+r9cky4GnA+8bdy36lwyouR0PXDe0v5sJ+AdL0K2u/wjgK+OtZH50Q11XATcAn62qSfje/w1YB/xy3IXMswI+k+SKbim5JhlQcxtpOSYtLknuCfwV8B+q6sfjrmc+VNVtVfVwBqu+nJxkUQ/rJnkGcENVXTHuWsbgcVX1SAZ3m3hlN5zfHANqbqMs2aRFpLsG81fA/6qqj4+7nvlWVT8CLgfmXMxzgXsccFp3PWYj8MQkHxpvSfOjqq7vnm8APsHgUkZzDKi5jbJkkxaJbrLA+4EdVXXBuOuZL0mWJrlPt/2vgCcD/zjeqvpVVedW1fKqegCD/68/X1UvGHNZvUtydDcBiCRHA78DNDlT14CaQ1XtBw4s2bQDuLiqto+3qvmR5CPAPwAPSbI7yUvHXdM8eBzwQgZ/TV/VPX533EXNg+OAy5JczeCPss9W1cRMu54wvwp8McnXga8Cn66qS8dc04ycZi5JapJnUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVBSz5K8oVsh/Opu2vpjkrwvyYnd6zcf5H2PTfKV7j07krx5XguXxqy3W75LgiS/BTwDeGRV/TzJ/YEjq+plI7z9IuDMqvp6t6r+Q/qsVWqNZ1BSv44D9lXVzwGqal9VXZ/k8iSrDzRK8vYkVyb5uyRLu8O/Any/e99tB275keTNST6Y5PNJvpXk5fP8naR5YUBJ/foMsCLJPyV5V5InzNDmaODKbvHOLwB/1B1/B7AzySeSvCLJUUPveRiD20T8FnBekl/r8TtIY2FAST3q7rH0KGAtsBf4aJIXT2v2S+Cj3faHgMd37z2fwY30PgM8DxhejuaTVXVLd8O5y2h0sU/p7vAalNSzqrqNwerglyf5BvCiud4y9N5vA+9O8l5gb5L7TW9zkH1pwfMMSupRkockWTV06OHA96Y1Oww4o9t+HoPbrZPk6d3q6gCrgNuAH3X7pyc5qgusUxgs8CotKp5BSf26J/A/ultZ7Ad2MRjuu2SozU+Ahya5Avhn4Dnd8RcC70jy0+69z6+q27rM+irwaeAE4K0H7u8jLSauZi4tMN3voW6uqj8bdy1SnxzikyQ1yTMoSVKTPIOSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNen/AxdLU40g8qIeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(x='SibSp', y='Survived', data=train, kind='bar', size=6, palette='muted')\n",
    "# g.despine(left=True)\n",
    "g.set_ylabels('survival probability')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Parch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1d8a6b454e0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pz2Sxz++64UjypiQfSfKoHvvOHJ/dORowyV7AHwOv7VHDyUm2Jtm6Y8eOHj8tSZp0fR638eaq+l6SJwC/ApwNvLvH97YDh85YX8voht+d9gMeAXwiydeBxwKb5hooUVUbq2pdVa1bvXp1j5+WJE26PgF1R/f+LODdVfVxYJ8e39sCHJnkiCT7ACcCm3ZurKrvVNVBVXV4VR0OXAIcV1Vb79ERSJKWpT4BdX2S9wAnAJuT3LvP96rqduAU4CLgKuD8qroiyelJjtuToiVJy1+fUXwnAOuBP6yqbyc5GHhdn51X1WZg86zP5nwab1Ud22efkqSVoc8ovluAj8xY/wbwjSGLkiSpzxnUijQ1NcX09DRr1qxhw4YN4y5HklYcA2oXpqenuf7668ddhiStWH0GSUiStOQMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTlsVksb922sWLvs+bbr4VgOmbb13U/X/w9Cct2r4kaTnzDEqS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1KRlMZv5EFbte8Bd3iVJS8uA2oUDH/7CcZcgSSuaXXySpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJg0aUEnWJ7kmybYkr59j+6lJrkxyWZJ/TPLAIeuRJE2OwQIqySrgTOAZwFHASUmOmtXsC8C6qvoF4AJgw1D1SJImy5BnUMcA26rq2qq6DTgPOH5mg6q6uKpu6VYvAdYOWI8kaYLsPeC+DwGum7G+HXjMPO1fBvztXBuSnAycDHDYYYctVn2SVojf+bsvLur+vnnLj+58X8x9v/Hpj1y0fS0HQwZU5vis5myYvAhYBzxxru1VtRHYCLBu3bo59yEtB/vd54C7vEsr2ZABtR04dMb6WuCG2Y2SPBV4I/DEqvrRgPVIzXvO0S8cdwlSM4a8BrUFODLJEUn2AU4ENs1skORo4D3AcVV144C1SJImzGABVVW3A6cAFwFXAedX1RVJTk9yXNfsD4D7AR9K8sUkm3axO0nSCjNkFx9VtRnYPOuz02YsP3XI35ckTS5nkpAkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDVp0IBKsj7JNUm2JXn9HNvvneR/d9s/m+TwIeuRJE2OwQIqySrgTOAZwFHASUmOmtXsZcC3quohwB8Dvz9UPZKkyTLkGdQxwLaquraqbgPOA46f1eZ44Oxu+QLgKUkyYE2SpAmRqhpmx8nzgPVV9Z+69V8HHlNVp8xoc3nXZnu3/tWuzU2z9nUycHK3+lDgmkGKvruDgJsWbDU5ltvxgMc0KTymybBUx3RTVa1fqNHeAxYw15nQ7DTs04aq2ghsXIyi7okkW6tq3VL/7lCW2/GAxzQpPKbJ0NoxDdnFtx04dMb6WuCGXbVJsjdwAPDNAWuSJE2IIQNqC3BkkiOS7AOcCGya1WYT8Bvd8vOAf6qh+hwlSRNlsC6+qro9ySnARcAq4H1VdUWS04GtVbUJeC9wTpJtjM6cThyqnt205N2KA1tuxwMe06TwmCZDU8c02CAJSZL2hDNJSJKaZEBJkppkQM1hoSmaJk2S9yW5sbvvbFlIcmiSi5NcleSKJK8Zd017Ksm+ST6X5EvdMb1t3DUthiSrknwhyV+Pu5bFkOTrSb6c5ItJto67nsWQ5P5JLkhydfdn6nHjrgm8BnU33RRN/wd4GqNh8FuAk6rqyrEWtgeS/DLwfeD9VfWIcdezGJIcDBxcVZ9Psh9wKfCcCf/vFOC+VfX9JPcCPgW8pqouGXNpeyTJqcA6YP+qeva469lTSb4OrJs9ocAkS3I28MmqOqsbdf1TVfXtcdflGdTd9ZmiaaJU1b+wzO4vq6pvVNXnu+XvAVcBh4y3qj1TI9/vVu/VvSb6X5BJ1gLPAs4ady2aW5L9gV9mNKqaqrqthXACA2ouhwDXzVjfzoT/xbfcdbPgHw18dryV7LmuO+yLwI3A31fVpB/T/wSmgJ+Mu5BFVMDfJbm0m4Zt0j0I2AH8edcVe1aS+467KDCg5tJr+iW1Icn9gA8D/6WqvjvuevZUVd1RVY9kNPPKMUkmtks2ybOBG6vq0nHXssgeX1WPYvSkhld1XeiTbG/gUcC7q+po4AdAE9feDai76zNFkxrQXaf5MPCXVfWRcdezmLoulk8AC06o2bDHA8d112zOA56c5APjLWnPVdUN3fuNwEcZXRaYZNuB7TPO1i9gFFhjZ0DdXZ8pmjRm3YCC9wJXVdUZ465nMSRZneT+3fJ9gKcCV4+3qt1XVW+oqrVVdTijP0f/VFUvGnNZeyTJfbtBOXTdYE8HJnp0bFVNA9cleWj30VOAJgYbDTmb+UTa1RRNYy5rjyQ5FzgWOCjJduAtVfXe8Va1xx4P/Drw5e6aDcBvV9XmMda0pw4Gzu5Gku4FnF9Vy2Jo9jLyM8BHu8fW7Q18sKouHG9Ji+K3gL/s/lF+LfDSMdcDOMxcktQou/gkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgpAEluaOb9fryJB9K8lOLsM+XJHnnYtQntcyAkoZ1a1U9sptF/jbglX2/2N0PJa1YBpS0dD4JPAQgyce6yUavmDnhaJLvJzk9yWeBxyV5dJLPdM+I+tzOWQyAn01yYZKvJNkwhmORBudMEtISSLI3o8lFd8468JtV9c1uSqMtST5cVTcD9wUur6rTurv6rwZeUFVbusci3Np9/5GMZnD/EXBNkj+tquuQlhEDShrWfWZMxfRJumfuAK9O8h+65UOBI4GbgTsYTYAL8FDgG1W1BWDnbO3dNDv/WFXf6davBB7IXR8TI008A0oa1q3d4zPulORYRhPBPq6qbknyCWDfbvMPq+qOnU3Z9aNefjRj+Q78s6xlyGtQ0tI7APhWF04PAx67i3ZXM7rW9GiAJPt1XYXSiuD/7NLSuxB4ZZLLgGuAS+ZqVFW3JXkB8KfdtapbGZ15SSuCs5lLkppkF58kqUkGlCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUn/H81FFWYeXJCPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(x='Parch', y='Survived', data=train, kind='bar', size=6, palette='muted')\n",
    "g.set_ylabels('survival probability')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(train, col='Survived')\n",
    "g = g.map(sns.distplot, 'Age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d8a6ca3fd0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.kdeplot(train['Age'][(train['Survived']==0) & (train['Age'].notnull())],\n",
    "               color='Red', shade=True)\n",
    "g = sns.kdeplot(train['Age'][(train['Survived']==1) & (train['Age'].notnull())],\n",
    "               color='Blue', shade=True)\n",
    "g.set_xlabel('Age')\n",
    "g.set_ylabel('Frequency')\n",
    "g.legend(['Not survived', 'Survived'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Fare'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Fare'] = dataset['Fare'].fillna(dataset['Fare'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.distplot(dataset['Fare'], color='m', label='Skewness : %.2f' % dataset['Fare'].skew())\n",
    "# g = g.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Fare'] = dataset['Fare'].map(lambda i: np.log(i) if i > 0 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d8a71a6a20>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(dataset['Fare'], color='b')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Categorial values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.barplot(x='Sex', y='Survived', data=train)\n",
    "g = g.set_ylabel('Survived probability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.747573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.190559</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Survived\n",
       "Sex             \n",
       "female  0.747573\n",
       "male    0.190559"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['Sex', 'Survived']].groupby('Sex').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1d8a7277550>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(x='Pclass', y='Survived', data=train, kind='bar', size=6, palette='muted')\n",
    "g.set_ylabels('survival probability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1d8a71471d0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 502.5x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(x='Pclass', y='Survived', hue='Sex', data=train, size=6, kind='bar', palette='muted')\n",
    "g.set_ylabels('survival probability')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Emabarked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Embarked'].isnull().sum()\n",
    "dataset['Embarked'].fillna('S', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    906\n",
       "C    270\n",
       "Q    123\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Embarked'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1d8a7284cc0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x='Embarked', y='Survived', data=train, size=6, kind='bar', palette='muted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAMMEiwAAAADAMMEiAAAAADBMsAgAAAAADBMsAgAAAADDBIsAAAAAwDDBIgAAAAAwTLAIAAAAAAwTLAIAAAAAwwSLAAAAAMAwwSIAAAAAMEywCAAAAAAMEywCAAAAAMMEiwAAAADAMMEiAAAAADBMsAgAAAAADBMsAgAAAADDBIsAAAAAwDDBIgAAAAAwbGbBYlUdXVUfqKqrq+rKqnrBNH6fqrqwqj47vd97Gq+qen1Vbauqy6rq0bOqDQAAAADYN7M8Y/HWJL/a3T+c5Pgkz62qhyZ5cZKLuvvYJBdN60ny+CTHTq8zkrxxhrUBAAAAAPtgZsFid1/X3Z+clr+e5OokRyY5Jcm507Rzk5w6LZ+S5Lxe8rEkh1fVEbOqDwAAAADYe2tyj8WqekCSRyW5OMn9u/u6ZCl8THK/adqRSb64bLPt0xgAAAAAsGBmHixW1T2TvDPJC7v7a3uausJYr7C/M6pqS1Vt2bFjx/4qEwCG6UkALAo9CYB5mGmwWFWHZilUfGt3v2savn7nJc7T+w3T+PYkRy/b/Kgk1+66z+4+q7s3d/fmjRs3zq54ALgTehIAi0JPAmAeZvlU6EpydpKru/u1yz66IMnp0/LpSd6zbPy06enQxyf56s5LpgEAAACAxbJhhvs+IcnTk1xeVVunsd9I8qok51fVs5Jck+RJ02fvS/KEJNuSfCPJM2dYGwAAAACwD2YWLHb3h7PyfROT5LErzO8kz51VPQAAAADA/rMmT4UGAAAAAA4sgkUAAAAAYNiqgsWqumg1YwAAAADAwWGP91isqrsn+a4k962qe+f2eybeK8n3z7g2AAAAAGBB3dnDW56d5IVZChE/kduDxa8l+f0Z1gUAAAAALLA9Bovd/XtJfq+qnt/db1ijmgAAAACABXdnZywmSbr7DVX1z5M8YPk23X3ejOoCAAAAABbYqoLFqnpLkgcl2Zrktmm4kwgWAQAAAOAgtKpgMcnmJA/t7p5lMQAAAADA+nCXVc67Isn3zbIQAAAAAGD9WO0Zi/dNclVVXZLklp2D3f1vZlIVAAAAALDQVhssvmyWRQAAAAAA68tqnwr9F7MuBAAAAABYP1b7VOivZ+kp0Ely1ySHJvmH7r7XrAoDAAAAABbXas9YPGz5elWdmuS4mVQEAAAAACy81T4V+jt09/9IctJ+rgUAAAAAWCdWeyn0LyxbvUuSzbn90mgAAAAA4CCz2qdC/9yy5VuTfCHJKfu9GgAAAABgXVjtPRafOetCAAAAAID1Y1X3WKyqo6rq3VV1Q1VdX1XvrKqjZl0cAAAAALCYVvvwljcnuSDJ9yc5Msn/nMYAAAAAgIPQaoPFjd395u6+dXqdk2TjDOsCAAAAABbYaoPFG6vqaVV1yPR6WpIvzbIwAAAAAGBxrTZY/LdJnpzk75Ncl+SJSTzQBQAAAAAOUqt6KnSS305yend/OUmq6j5JfjdLgSMAAAAAcJBZ7RmLj9wZKiZJd9+U5FGzKQkAAAAAWHSrDRbvUlX33rkynbG42rMdAQAAAIADzGrDwdck+auqekeSztL9Fs+cWVUAAAAAwEJbVbDY3edV1ZYkJyWpJL/Q3VfNtDIAAAAAYGGt+nLmKUgUJgIAAAAAq77HIgAAAADAP/EAFphc84pHzLsEVumYl14+7xIAAADgoOeMRQAAAABgmGARAAAAABgmWAQAAAAAhgkWAQAAAIBhgkUAAAAAYJhgEQAAAAAYJlgEAAAAAIYJFgEAAACAYYJFAAAAAGCYYBEAAAAAGDazYLGq3lRVN1TVFcvGXlZVf1dVW6fXE5Z99pKq2lZVn6mqn51VXQAAAADAvpvlGYvnJDl5hfHXdfem6fW+JKmqhyZ5SpKHTdv8QVUdMsPaAAAAAIB9MLNgsbs/lOSmVU4/Jcnbu/uW7v58km1JjptVbQAAAADAvpnHPRafV1WXTZdK33saOzLJF5fN2T6N3UFVnVFVW6pqy44dO2ZdKwDslp4EwKLQkwCYh7UOFt+Y5EFJNiW5LslrpvFaYW6vtIPuPqu7N3f35o0bN86mSgBYBT0JgEWhJwEwD2saLHb39d19W3d/O8kf5fbLnbcnOXrZ1KOSXLuWtQEAAAAAq7emwWJVHbFs9eeT7Hxi9AVJnlJVd6uqByY5Nskla1kbAAAAALB6G2a146p6W5ITk9y3qrYn+a0kJ1bVpixd5vyFJM9Oku6+sqrOT3JVkluTPLe7b5tVbQAAAADAvplZsNjdT11h+Ow9zD8zyZmzqgcAAAAA2H/m8VRoAAAAAGCdEywCAAAAAMMEiwAAAADAMMEiAAAAADBMsAgAAAAADBMsAgAAAADDBIsAAAAAwDDBIgAAAAAwTLAIAAAAAAwTLAIAAAAAwwSLAAAAAMAwwSIAAAAAMEywCAAAAAAMEywCAAAAAMMEiwAAAADAMMEiAAAAADBMsAgAAAAADBMsAgAAAADDBIsAAAAAwDDBIgAAAAAwbMO8C5iHx7zovHmXwCp94tWnzbsEAAAAAFbgjEUAAAAAYJhgEQAAAAAYJlgEAAAAAIYJFgEAAACAYYJFAAAAAGCYYBEAAAAAGCZYBAAAAACGCRYBAAAAgGGCRQAAAABgmGARAAAAABgmWAQAAAAAhgkWAQAAAIBhgkUAAAAAYJhgEQAAAAAYJlgEAAAAAIYJFgEAAACAYYJFAAAAAGCYYBEAAAAAGCZYBAAAAACGCRYBAAAAgGEzCxar6k1VdUNVXbFs7D5VdWFVfXZ6v/c0XlX1+qraVlWXVdWjZ1UXAAAAALDvZnnG4jlJTt5l7MVJLuruY5NcNK0nyeOTHDu9zkjyxhnWBQAAAADso5kFi939oSQ37TJ8SpJzp+Vzk5y6bPy8XvKxJIdX1RGzqg0AAAAA2DdrfY/F+3f3dUkyvd9vGj8yyReXzds+jd1BVZ1RVVuqasuOHTtmWiwA7ImeBMCi0JMAmIdFeXhLrTDWK03s7rO6e3N3b964ceOMywKA3dOTAFgUehIA87DWweL1Oy9xnt5vmMa3Jzl62byjkly7xrUBAAAAAKu01sHiBUlOn5ZPT/KeZeOnTU+HPj7JV3deMg0AAAAALJ4Ns9pxVb0tyYlJ7ltV25P8VpJXJTm/qp6V5JokT5qmvy/JE5JsS/KNJM+cVV0AAAAAwL6bWbDY3U/dzUePXWFuJ3nurGoBAAAAAPavRXl4CwAAAACwjggWAQAAAIBhgkUAAAAAYJhgEQAAAAAYJlgEAAAAAIYJFgEAAACAYYJFAAAAAGCYYBEAAAAAGCZYBAAAAACGbZh3AQBwMHvMi86bdwms0idefdq8SwAAgIXijEUAAAAAYJhgEQAAAAAYJlgEAAAAAIYJFgEAAACAYYJFAAAAAGCYYBEAAAAAGCZYBAAAAACGCRYBAAAAgGGCRQAAAABgmGARAAAAABgmWAQAAAAAhgkWAQAAAIBhG+ZdAAAAAADrxwlvOGHeJTDgI8//yMz27YxFAAAAAGCYYBEAAAAAGCZYBAAAAACGCRYBAAAAgGGCRQAAAABgmKdCAwDAOuAJnOvLLJ/ACQCLwhmLAAAAAMAwwSIAAAAAMEywCAAAAAAMEywCAAAAAMMEiwAAAADAMMEiAAAAADBMsAgAAAAADBMsAgAAAADDBIsAAAAAwDDBIgAAAAAwTLAIAAAAAAwTLAIAAAAAwzbM40ur6gtJvp7ktiS3dvfmqrpPkj9J8oAkX0jy5O7+8jzqAwAAAAD2bJ5nLP5Ud2/q7s3T+ouTXNTdxya5aFoHAAAAABbQIl0KfUqSc6flc5OcOsdaAAAAAIA9mFew2En+T1V9oqrOmMbu393XJcn0fr851QYAAAAA3Im53GMxyQndfW1V3S/JhVX16dVuOAWRZyTJMcccM6v6AOBO6UkALAo9CYB5mMsZi9197fR+Q5J3JzkuyfVVdUSSTO837Gbbs7p7c3dv3rhx41qVDAB3oCcBsCj0JADmYc2Dxar67qo6bOdykp9JckWSC5KcPk07Pcl71ro2AAAAAGB15nEp9P2TvLuqdn7/H3f3+6vq40nOr6pnJbkmyZPmUBsAAAAAsAprHix29+eS/MgK419K8ti1rgcAAAAAGDevp0IDAAAAAOuYYBEAAAAAGCZYBAAAAACGCRYBAAAAgGGCRQAAAABgmGARAAAAABgmWAQAAAAAhgkWAQAAAIBhgkUAAAAAYJhgEQAAAAAYJlgEAAAAAIYJFgEAAACAYYJFAAAAAGCYYBEAAAAAGCZYBAAAAACGCRYBAAAAgGGCRQAAAABgmGARAAAAABgmWAQAAAAAhgkWAQAAAIBhgkUAAAAAYJhgEQAAAAAYJlgEAAAAAIYJFgEAAACAYYJFAAAAAGCYYBEAAAAAGCZYBAAAAACGCRYBAAAAgGEb5l0AAADf6ZpXPGLeJbBKx7z08nmXAAAwN85YBAAAAACGCRYBAAAAgGGCRQAAAABgmGARAAAAABgmWAQAAAAAhgkWAQAAAIBhgkUAAAAAYJhgEQAAAAAYJlgEAAAAAIYJFgEAAACAYYJFAAAAAGCYYBEAAAAAGCZYBAAAAACGbZh3AbuqqpOT/F6SQ5L8t+5+1ZxLAgAAgIPSNa94xLxLYJWOeenl8y6Bg9BCBYtVdUiS30/y00m2J/l4VV3Q3VfNtzIAAIAD12NedN68S2DAJ1592rxLAEiyeJdCH5dkW3d/rrv/Mcnbk5wy55oAAAAAgF1Ud8+7hn9SVU9McnJ3//K0/vQkP9bdz1s254wkZ0yrD0nymTUvdDHdN8mN8y6CheTYYCWOi9vd2N0n782GetJuOb7YHccGK3Fc3E5P2v8cX+yOY4OVOC5ut6qetGjB4pOS/OwuweJx3f38+Va2+KpqS3dvnncdLB7HBitxXDBLji92x7HBShwXzJLji91xbLASx8W4RbsUenuSo5etH5Xk2jnVAgAAAADsxqIFix9PcmxVPbCq7prkKUkumHNNAAAAAMAuFuqp0N19a1U9L8mfJTkkyZu6+8o5l7VenDXvAlhYjg1W4rhglhxf7I5jg5U4Lpglxxe749hgJY6LQQt1j0UAAAAAYH1YtEuhAQAAAIB1QLAIAAAAAAwTLK5zVfWmqrqhqq6Ydy0sjqo6uqo+UFVXV9WVVfWCedfEYqiqu1fVJVV16XRsvHzeNXHg0JNYiZ7E7uhJzJKexEr0JHZHT9p77rG4zlXVv0xyc5Lzuvvh866HxVBVRyQ5ors/WVWHJflEklO7+6o5l8acVVUl+e7uvrmqDk3y4SQv6O6Pzbk0DgB6EivRk9gdPYlZ0pNYiZ7E7uhJe88Zi+tcd38oyU3zroPF0t3Xdfcnp+WvJ7k6yZHzrYpF0EtunlYPnV7+wsR+oSexEj2J3dGTmCU9iZXoSeyOnrT3BItwgKuqByR5VJKL51sJi6KqDqmqrUluSHJhdzs2gDWhJ7ErPQmYFz2JXelJe0ewCAewqrpnkncmeWF3f23e9bAYuvu27t6U5Kgkx1WVy4OAmdOTWImeBMyDnsRK9KS9I1iEA9R0X4h3Jnlrd79r3vWweLr7K0k+mOTkOZcCHOD0JO6MngSsFT2JO6MnjREswgFouvHs2Umu7u7XzrseFkdVbayqw6fleyR5XJJPz7cq4ECmJ7E7ehKw1vQkdkdP2nuCxXWuqt6W5KNJHlJV26vqWfOuiYVwQpKnJzmpqrZOryfMuygWwhFJPlBVlyX5eJbuHfLeOdfEAUJPYjf0JHZHT2Jm9CR2Q09id/SkvVTdHnIDAAAAAIxxxiIAAAAAMEywCAAAAAAMEywCAAAAAMMEiwAAAADAMMEiAAAAADBMsAjrQFXdVlVbq+qKqvrTqvquPcx9WVX92lrWB8DBQ08CYFHoSTB/gkVYH77Z3Zu6++FJ/jHJc+ZdEAAHLT0JgEWhJ8GcCRZh/fnLJA9Okqo6raouq6pLq+otu06sql+pqo9Pn79z51/wqupJ01/1Lq2qD01jD6uqS6a/+F1WVceu6a8CYD3SkwBYFHoSzEF197xrAO5EVd3c3fesqg1J3pnk/Uk+lORdSU7o7hur6j7dfVNVvSzJzd39u1X1vd39pWkfr0xyfXe/oaouT3Jyd/9dVR3e3V+pqjck+Vh3v7VxC3SEAAABgklEQVSq7prkkO7+5lx+MAALS08CYFHoSTB/zliE9eEeVbU1yZYk1yQ5O8lJSd7R3TcmSXfftMJ2D6+qv5wa5C8ledg0/pEk51TVryQ5ZBr7aJLfqKpfT/IDmiUAu6EnAbAo9CSYsw3zLgBYlW9296blA1VVSe7slONzkpza3ZdW1TOSnJgk3f2cqvqxJP8qydaq2tTdf1xVF09jf1ZVv9zdf76ffwcA65+eBMCi0JNgzpyxCOvXRUmeXFXfmyRVdZ8V5hyW5LqqOjRLf4nLNPdB3X1xd780yY1Jjq6qf5bkc939+iQXJHnkzH8BAAcKPQmARaEnwRpyxiKsU919ZVWdmeQvquq2JJ9K8oxdpv2nJBcn+dskl2epgSbJq6ebDleWGu+lSV6c5GlV9f+S/H2SV8z8RwBwQNCTAFgUehKsLQ9vAQAAAACGuRQaAAAAABgmWAQAAAAAhgkWAQAAAIBhgkUAAAAAYJhgEQAAAAAYJlgEAAAAAIYJFgEAAACAYf8fe+XkCJlfOoIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot('Pclass', col='Embarked', data=train, size=6, kind='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.Filling missing Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Age "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 330.375x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(y='Age', x='Sex', data=dataset, kind='box')\n",
    "g = sns.factorplot(y='Age', x='Sex', hue='Pclass', data=dataset, kind='box')\n",
    "g = sns.factorplot(y='Age', x='Parch', data=dataset, kind='box')\n",
    "g = sns.factorplot(y='Age', x='SibSp', data=dataset, kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.heatmap(dataset[['Age', 'Sex', 'SibSp', 'Parch', 'Pclass']].corr(), cmap='BrBG', annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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opZg/73OOyfSTmmRsiE+IzsxesGCBodcxgw6YGHv//fdpamzEn3usodcJZg0CVwqvvPqqodfpqjZt2kRZeQUn9DCu9dIixxumb2qY+fPnGX6teNMBE0ONjY288cabBNPzCCd3N/ZiNjvNPY9h/bp1rF692thrdUELFy7ELjCmgxMbW+v47s1s3Lgp4YYf6ICJoQ8++IC6ulp8vUbH5XqB7KHg9PLiSy/F5XpdyeJFCxnWLUCywbdHLcZEB/F99dVXcblevOiAiZGmpiZemzmTUHpvwilx2kvO5qC550jWrF6tWzExVFJSQlFxSdxaLwB5ySGyvSrhJrPqgImR999/n7raWpp7HRfX6wZ6DIu0Yl58Ma7XTWQtW7x2dN2XthCB4zKb+WblyoSazKoDJgYirZfX49t6adHSilmzJrJyntZhixctondKmGxPbGZOt9bo7gH8gQArV66M63WNpAMmBiKtl5q4t15a7G/F6L6YDqutrWXtunWMzYp/K+KobgG8SSTUbZIOmA5qbm7m9TfeIJTWK/6tlxY2B809j2H1qlVdYhlGIy1evJhwOLy/0zWeHDY4NtPH4kULE2YaiA6YDpo9ezY11dVxe3J0KIHsYUiSm5dfecXUOjq7+fPnke1RDEg1Zu7RkYzv4aOmto5VCbLAuw6YDggEAsyc+Tqh1BxCqTnmFmNPornH0axYvpz8/Hxza+mkKisrWbFiJeN7NMdyAnybjMwM4EkiYVa60wHTAXPnzqWiohyfwaN2W8vfYzjicDFjxgyzS+mU5syZQzgc5pQc857iOO1wYo/Ier11dXWm1RErOmDaKRQK8drMmajkLEId3DEgZhxOmrOH8eXChRQXF5tdTacSDAb58IP3Oapb7Nbdba/To+v1zpkzx9Q6YkEHTDstXryYXSUlNPccGdMFpToq0PNoEBtvvPGG2aV0KgsWLGDP3n2cnWfc0gyt1T81xNBuQd568w0CgYDZ5XSIDph2UErx2mszwZ1GMLO/2eV8h0ry4M8awpxPPkm4eS1GCYVCvDbjVXolq5ivu9teF/VrpKy8otP3xRwxYESkp4g8LyIfR98eISI/N74061q7di1btmymOdpasBp/zjGEQiHeeecds0vpFD755BMKt+/g0v712CzSGD02M8Cg9BAv/Os5GhsbzS6n3Vrz0/ES8AnQK/p2PnCLUQV1BjNnvo44PQS6DzG7lINS7jQC3frxn/feS9i1XmOloaGBfz03ncHpIcbHYWmG1hKBnw6up6KqmpkzZ5pdTru1JmC6K6XeAsIASqkgYM4gAQsoLCxk6dIlNGcPB5vD7HIOyZ8zkqbGxoResT4Wnn32Waqqq/nZkDordaUBMCQ9yEk9fbz++kwKCgrMLqddWhMwDSKSBSgAEZkA1BhalYXNnDkTsSfh7zHc7FIOK5ySTSgtlzfefAu/3zq/ma1kxYoVzJo1i/P6NDEwzZq/M68e2kCyI8wjj/ypU3b4tiZg/g/4ABgkIouBV4DftPYCImIXkVUiMiv69gARWSoiW0XkTRGJ/arYBikpKWHevHn4ug8Dh8vsco7IlzuK6qpKPv74Y7NLsZyKigr++NCD9EpW/HiAdfs4UpMU1w2tpaBgG9OnTze7nDY7YsAopb4BTgNOAm4EjlZKrW3DNW4GNh3w9qPAE0qpIUAV0Gk6jGfMmIHChj/3GLNLaZVQai7hlB68OmNGp/ztZ5RgMMhDDz1IQ10tvz66Bqfd7IoOb2x2gLPymnj77bdZuHCh2eW0SWueIv0YuBgYBgwFLhKRM0XkiDP7RCQPuAD4V/RtASYB/44e8jJwaftKj6+SkhI+/fRTfNlDUUles8tpHRGae42mvKyMjz76yOxqLOOf//wnq1at5pqhdeSlWPPW6PuuGtzIwLQQD//xIQoLC80up9Vac4v0cyIBcXX0z3NEbpsWi8j/O8Jr/wbcQbSDGMgCqqMdxQAlwEGHwYrIDSKyQkRWlJWVtaJMY73wwgsoseHPHRXT87qKlmBvrMDeWIFn80e4imK7sX0orTfh1J68+NLLCbWQUXvNmjWLf//735yT18QpubHfrdEoSTb435G1uPAx9e67qK6uNrukVmlNwISB4Uqpy5VSlwMjAB8wHrjzUC8SkQuBUqXUgavnHKyf/qCLniqlpiulximlxmVnZ7eiTONs3ryZefPm0dxjBCrJE9Nz2xorkVAACQVw1O3F1lgZ0/MjQnPvsVRXVfL222/H9tydzPLly3niiccZmRngqsHW7Xc5lExXmJuPqaGivJSpU+/uFL8wWhMw/ZVS+w54uxQYqpSqBA53Y38ycLGI7ADeIHJr9Degm4i0PN/NA3a3ueo4Ukrx1NNPI06P4VuRGCWUmkMwox8zZrzWZUf35ufn8/vf3Utvb5BfH1OH3XrjI1tlUFqQm0bUsWnjJh544AHLrxvTmk/zQhGZJSLXiMg1wPvAlyKSDByynaaUulsplaeU6g9MAeYppa4G5gNXRA9rOZ9lffbZZ6xft46mXmMM2QY2XprzjscXCPDss8+aXUrc7dq1izvv+C1e8XHbsdV4HJ17N8xx2X5+NrSBr776iieeeMLSu3u2JmD+B3gRGB39swxQSqkGpdQZ7bjmncD/iUgBkT6Z59txjrioq6vjqaefJpySTaD7ULPL6RDlTsOXM5LPPvuMb775xuxy4qaiooLb/+9WAo01/PbYKjJc1v1hbIuz85q5uF8js2fP5vnnLfsj1KrH1ArYRuR26DLgTL772PmIlFJfKKUujP67UCl1glJqsFLqSqWUZXvann76aWpqamjqe6KlZky3lz/3WHCn8eif/0xTk/mzho1WV1fHb2+/jcqKUm47toZeJi/DEGuXD2zijF7NzJgxw7L9a4cMGBEZKiK/F5FNwFNAMSBKqTOUUk/FrUKTLFmyhDlz5uDLGWn8Lo3xYnPQ2H8i+/bu7ZSDttrC54s8bdm5cwc3H1PLoDTj+ypm5HvZWWdnZ52dh79JY0a+scMZROCaYQ0cn+3n6aeftuTM68O1YDYTaa1cpJSaqJR6ki4yB6myspKH//QIypuJ36SdAowSSs3B33ME//nPf1i6dKnZ5RgiFArxwAP3s379Bn45vI5jMuMzyLCo3kFTyEZTyMbm6iSK6o2fq2YTuHFEHcMzgjz66CMsW7bM8Gu2xeEC5nJgLzBfRJ4TkTM5+GPmhBIOh3nkkUeoq6unccBpYLP4MM928OWNQ3kz+ePDf0q4p0pKKZ588kkWL/6Kq4c0ML5n4s/DctrhlpG19PYG+cPvf8fWrVvNLmm/QwaMUuo/SqnJwFHAF8CtQE8ReVZEzolTfXE3Y8YMli1bRlOf4wl7M8wuxxg2B40DT6OuvoH77rvf8o862+Ltt9/mvffe47y+TZzTx/rjRGLF41Dcdmw1XvFx1x2/pbS01OySgNZ18jYopV6LdtLmAauBuwyvzARLly7lhRdfJJA1iED2UWaXY6iwJ4PGfiexbt1apk2bZnY5MbFs2TL++eyzHJ/tY/KgzjeQrqMyXIrbRlbTUFfN7+69B5/P/OcnbRpupJSqVEpNU0pNMqogsxQVFXHf/fejPBk09zspIZ4aHUkwaxD+HsN5++23O/0C07t27eKB+++jd0qIG0ZYZ2W6eMtLCXHj8Fq25G/lr3/9q+ljZDrpeMbYqq2t5a6776Y5qGgYfBbYk8wuKW58fcYTSsvlL4891ml3hQwEAjxw/32E/Y3cckwNrsTrNmuTsdkBLu3fyKeffsonn3xiai1dPmD8fj/33Hsvu/fspWHgGShXitklxZfNRuOgSYSSkrl76lRKSkrMrqjNXnjhBbbkb+Xnw+rivmG9VV06oIlh3YL87YknTP2adumAUUrxl7/8hXVr19LUf6L5uzOaxeGifvBZNDQHuOOOOzvNTF2IzDF68403OC23meMttKau2WwCN42owxb28de/PmbarVKXDpjnn3+euXPn4us9hmDWILPLMZVyp9MwaBJ79u5l6lRrdBAeSTgc5vHH/0qKUzGlE86ONlqmO8wVA+pZtWo1n3/+uSk1dNmA+fDDD5kxYwb+7KExX+Olswql5tA44DQ2btzAQw89RChk7XGVCxYsYPPmLUwZWEdyUmLMMYq1Sb19DEgLMX3aP01Z1bBLBsxXX33F448/TjA9D18XeWLUWsHM/jT3Gc/ChQt55plnzC7nkMLhMK+8/BK9khUn5ehbo0OxCfx4QAOlZeWmTCXocgGzZcsW7rvvfkLeLJoGnWHJjdPMFsg5Gn/Po3nnnXcsO4lu5cqVbN+xk4v6dd1H0q11bGaAAWkh3nrj9bj3xXSpn67S0lLuvOtu/JJE45Cu9Ti6rXx9TiCY0Y+nn3mGRYsWmV3OD3zyySd4k+AE3bF7RCJwem4TO4tLyM/Pj+u1u0zANDU1ceddd1FTV0/D4LM7z8LdZhGhacBphJO788ADD1pq4y+/38/ChV8yPruZpC7zHdwxJ/Tw47DBvHnz4nrdLvHlUUrxyCOPsH37dhoGnp64c4xize6gcfCZ+MXB3VOnWubx9ZYtW/D5/IzM0luxtFZykmJQWpDVq1fF9bpdImDeeustFixYQHPvcYTS88wup1NRSV4aBk6ivLyCBx54wBJPljZs2ADA0HQdMG0xNN3P1q0FcR2CkPABs2HDBqZNm04wox+BnM6xYZrVhFOyaeo7gW+++YbXXnvN7HLYtWsXqS4hzakfTbdFr+QQ4XCYvXv3xu2aCR0wjY2N3P/Ag4SdXpr6T9SPozsg0H0ogcyBvPjii2za1KYVU2Nu3759ZLnMb0l1Nt3dkWkU8VzKIaEDZvr06ZTu20tj/1Osu5d0yI/b7eaKK67A7XZDyKJPRURo7ncSypnMww//ydSRvj6fD7fNegHTFJTvfC2bgtb6heayR1p8+hYpBjZv3sx7772Hv8cIS88xkqCfCy+8kF//+tdccMEFSNCiAQPgcNLY7ySKi4t48803TS3FijdHjUH5ztey0WIB0yKeY2GMXzTUBJFlE59CnB58eWPMLuewlMPJrFmzUEoxe/ZslMPaj89D6XkEM/rx2mszOf/88+nePf4Lonu9XqpD1luTwetQ3/la9rTY/kstLSqvN37fYwnZglm2bBkbNqynKfc462+WZnfS3NzMO++8E9kK1Or1Et3Eze/n9ddfN+X62dnZVPisFzAeh/rO19JqG7xV+iI/7j169IjbNRMyYN54401wJRPoPsTsUhKScqcRyBzIrFmzqa2tjfv1e/XqRb1fUeu35i2IVe1usGO323TAdMTu3btZteobfN2PSsgdAazCn3M0Pl9z3EeGAgwfPhyAwtqEvMM3zLbaJAYOGIjLFb8HHgkXMAsXLgQgkDXQ5EoSW9iTCZ5uLPjyy7hfe+jQoSQ57Gyo0nPJWssXgm21To4ZOTKu1024gFm1ahXKm4FypZpdSmITwZ/Wm7Vr18Z92xOPx8OYMWNZVeHGwvu+W8qGyiR8IcXJJ58c1+smXMBs37GDoLub2WV0CSFvJqFgMK4jQ1uccuqplDYK2/RtUqt8tc9Fakoyo0bFd3G1hAuY6qpqPVM6Tlo+z5WVlXG/9qRJk/C4XczbZdEBlBZS4xdWlrn40bnnkZQU39vKhAuYJGcSKOuN8kxI0c+z0xn/R+ter5dzfnQuS0vd+x+/agc3t8RNSMHFF18c92sn3Feme1Z37M3xf3TaFdmin+fMzExTrj9lyhTCYmPWDo8p1+8M6gLC3BIvp512Kn379o379RMuYMaOHYOjoRTCibPfslU5anfTq3fvuI6rOFBubi7nnXc+8/e42duYcN/KMfHBDg/NIbj22utMuX7CfVVOOeUUVChIUukWs0tJaLamKhy1uzjt1FNNreO6667D5XLzan6KfqL0PUX1duaWeLjgggsZMGCAKTUkXMCMHj2a444bg2ffWgg2m11OYlIKV/EK3G43kydPNrWUrKws/vvnv2BdZRJf77P+NIt4CYXhxS2ppKSkcP3115tWh2EBIyJ9RGS+iGwSkQ0icnP0/ZkiMldEtkb/jvn6lb/61U3YwgG8274ApbcSjTXn3nU4aoq57tpr6dbN/CEBl156KSNGDOeVramUNyfc78x2+XAfj0tGAAAV0UlEQVSnh201dm6+5VbS09NNq8PIr0YQuE0pNRyYAPyPiIwA7gI+V0oNAT6Pvh1TQ4YM4fbbbsNeuxvXzq/RbefYcVRux1WygjPOOIOf/OQnZpcDgMPh4N57f4eyu5i2MZVgF/+dsqXawXs7vJx11lmceeaZptZiWMAopfYopb6J/rsO2AT0Bi4BXo4e9jJwqRHXP++887j66qtxlm3Bve0LCOtH1x2VVLoJz7b5jBhxNHfccQdioRUCe/XqxW23/5Yt1Q5eL+i646Aqm208tSGd3NxcbrnlFrPLiU8fjIj0B44DlgI9lVJ7IBJCwEEfQYjIDSKyQkRWlJWVteu6119/Pb/85S9JqtqON/8TxN/QrvMYKezNRNmTUPYkgqk5hL3mPPI9rHAIV9FS3Du/ZsKECTz++F/xeKz3aPiss87iJz/5CXNLPHyxu+sNwPOF4B/r0/CLiz8+/CdSUlLMLsn4BadEJAV4B7hFKVXb2t96SqnpwHSAcePGtfseZ8qUKXTv3p0///kvODa+T0P/iYS6xX88wKH4+k7A1hgZCdt01PkmV/ND0lyDt3ABtoZyLrnkEn7zm9/gcFh3eP4NN9zA9u2FvLRiBd2cYUZ37xo7D4TC8NT6VHbUOXjwod/Tv39/s0sCDG7BiEgSkXB5TSn1bvTd+0QkN/rxXMDwFYjPOuss/vWv5xjQtzferZ/h2r4IguatKdspqDBJ+zaQuvEDUmnmwQcf5NZbb7V0uECkP+b++x9g8ODBPLUhja011q43FsIKXtiSzJoKJzffckvcJzQejpFPkQR4HtiklHr8gA99AFwT/fc1wPtG1XCgvn378s9nn+Wqq67CXVlA2vp3cJRv1R3AB2FrKCd50yzcRUsZO2Y0L774AqeccorZZbWa1+vl0T//heyeuTy2Np3C2sRdF0gpeCU/mYV73FxzzTVccsklZpf0HUa2YE4G/h8wSURWR/+cDzwCnC0iW4Gzo2/HhdPp5MYbb+S5555j2OABeLYvJHnLx9gayuNVgqVJoAnXjsUkb/qQTGeI3//+9/zlz382baRuR2RkZPDE3/5Ot8we/HlNN3bUGR8yfVOCeOxhPPYwR3UL0DfF2NHkSsFrW73M2+Xmqquu4tprrzX0eu0h8VxhvL3GjRunVqxYEdNzhsNhPvroI6ZNf4662hoCWYPx5Y1FOZNjep3W8Gz+CDCxDyYcxLlvA+6965BwiMsuu5TrrrvOEp2EHbV3715u/t/fUFdVxm9H1TAozdgf+oe/SQNg6hhj58OFFbyan8znu9xceeWV/OpXv4r3U71WXazLjkqy2WxceOGFvPH6zMhtU80OUte9g7N4Rdfpn1FhHOVbSV3/Lq6SlZx4wjhefvklfvOb3yREuADk5OTw9388SXpWT/68Op0t1Z2/Tyas4MXNkXCZMmWKGeHSal02YFokJydz4403MuPVV5l0xmm49q4lbf07JO1dn7gTJpXCXl1MysYP8GxfyJC+uTz++OM8/PDD9OnTx+zqYi4nJ4d/PPkU3XN689iadNZXdt6lNoNh+OfGFBbscfNf//Vf3HjjjZYNF9ABs19ubi6/+93veO655xgz6hjcxctIXf8ujvKChOoIttWX4t3yMd6tc8lNd/GHP/yBadOmMWaMtfeP6qjs7Gz+/o8n6d2nP4+vTWNlWecLGX8o8ih6yT4X119/Pf/93/9t6XABHTA/MGTIEP762GM89thjDO6Ti2f7l6Rs+gB7TYnZpXWINNfiLphH8qZZZEgTN998MzNefZUzzjgDm61rfBtkZmbyt3/8g8FDhvLk+jSWdqLJkb4Q/G1dGt+UO7n55pu5+uqrzS6pVTr/DalBxo0bx/TpY5g/fz7Tpj9Haf6nBNPz8PU5gbDH/Al+rRb04dq9BmfZRlxJTqZccw2TJ0+O6+5+VpKWlsbjT/yNu+68g2fWryeo6jg5x8Lb9RIJl8fXprO52sGdd97JeeedZ3ZJrdY1fnW1k81m48wzz2TGq69w0003keqvJHnDe7iKlli/I1iFSSrdTNqGd3GWbuD8c89l5szXuO6667psuLRoGSczevRopm9M5au91m3JtITLluokpk69p1OFC+iAaRWn08nkyZOZOfM1LrzgfFylm0jb8B8cldst2T9ja6wiefNHuHd+xdHDBjN92jTuuOMOsrKyzC7NMjweD4888iijjxvN9E2pLC+1XsgEwvCPdWlsrnZwz733cvbZZ5tdUpvpgGmDjIwMbr/9dqZPn86gfr3xbJuPp+Bz60yiDIdwlqwkeeP7pNuaueeee3jyH/9g6NChZldmSS6Xiz/+8WGGDx/BMxtT2VhpnR6DsIJpG1NYV5nEHXfcafqyC+2lA6YdhgwZwj+ffZabbroJb+M+Uje+j726yNSapLmWlM2zce1Zwzlnn8Wrr7zC2WefbfmnDGbzer088uij5OX14ckN6ZZZ2/fd7R6Wlbr45S9/2eluiw5kjc9mJ+RwOJg8eTIvvPA8A/vmRSZRFi2FcPxXO3JUFpK66QNSaOahhx5i6tSpllhprrNITU3lT488isOTyhPruuEzeemgZaVOPtjh5fzzzzd9SdKO0gHTQXl5eTz77DNcdtllOPdtwFPwGYTitESAUjh3r8Gz7QuGDx3CCy88z8SJE+Nz7QTTq1cv7rv/AfY2Cm8WxH+6SItqn/BSfipHHTWMW2+9tdO3QHXAxIDTGRmbcNttt5FUu4vk/DnGLziuFK6ipbh2reTMM8/k73//Gz179jT2mgnuuOOO4/LLr+CzXW42VZnTH/Nyfgp+FXliFO9dGI2gAyaGLrroIh588EGczdUkF3xm6FQD565vcJZu5Morr+SeexLjm9EKrr/+enpkd+etwvhvg1JQ42BlmZNrrr3WlE3SjKADJsYmTpzIvffeg62+DHfhl4Y8xk4q24JrzxouuOACfvWrX3WZkbjx4HK5uPpn/49tNXY2VMU3tN/f4SEtNYXLLrssrtc1kv7ONMDpp5/OL2+8kaSqHSSVxXYDOFtTNe6iJYwdNy4h7tGt6LzzziMtNYXFe+O3rm+tX1hb4eSSSy9LqIGQOmAMMnnyZMaMGYOnZDniq4/NSZXCs2MRyR4PU+++2/LLV3ZWTqeTE8ZPYG2li3CcbpPWViShIOE66XXAGEREuOOOO3DYwLl7VUzO6ajaga2+lF//+n/0qFyDjR07ljo/7IvTuJiC2iRSk70MGTIkLteLFx0wBsrJyeHiiy/GWbEN8dV17GRK4d6zhrw+fTjnnHNiU6B2SLm5uQBU+OKznm9Fs42eubkJ15+WWP8bC5o8eTKCIqksv0PnsdWXIo2V/PSqq7DbE3cRa6vIyIjsaFzrj08fV13ATmZm4rVKdcAYrEePHhw3ZgyuqsIOPVFKqthGktPJ6aefHrvitEMKhSLDeR1x+gmxi9p/zUSiAyYOzpw0CZrrsDVVt+8ESuGqLeHECRMS6gmDlTU3RwZKOm3x6eVNsoVpamyMy7XiSQdMHBx//PEA2Gt3tev1tuYalK9+/3k047VsV5zhis/csgxXmLIyw/cgjDsdMHHQo0cPcnJysdfta9fr7fWR140ePTqWZWmHsW9f5HPe3R2fgOnuDlNRUUkwmFgLzeuAiZNjjjkaZ1P7Nniz1ZeSnJJKXl5ejKvSDqWkpIRkp5CcFJ9bpB6eMGGl2LNnT1yuFy86YOLkqKOOQvkaEH/b77OTmioZftQwPWo3jnbt2kVPd/xaEzneSAdvSUnnXlz++3TAxEnLqnK2xoq2vTAcQhqr9Kp0cVa6bw9ZrvgFTFa0r6el7ydR6ICJk8GDBwNgb2PA2JqqQIV1wMRZRUUl3eLUwQuQ5gwjQEVFG38BWZwOmDjxer306t0bW0Pb+mHs0eMTbQi51fn8ftz2+K3X4LBBkh18PovvVtFGOmDiaMTw4TibKto04M7WUEZKaiq9evUysDLNCqy3P0XH6YCJoxEjRkQ7elu/C0FSYzkjho/QHbxxluz1UheI34+HLwSBUGSv9ESiAyaORo4cCYC9bm+rjpdAM9JYxahRxxpZlnYQffr0ZV9j/OZ87Y1eq0+fPnG7ZjzogImjQYMG4U1Oxl7XurEOLUE0atQoI8vSDmLQ4MFsr3cSiFM/79aayNo+AwcOjM8F40QHTBzZbDbGjR2Ls25Pq/ph7DUleDxejjrqqDhUpx3opJNOojmo2FgZn2UzV5a7yOvdS7dgYkFEzhWRLSJSICJ3mVGDWcaPHw+++sjj58NRCmfdLsaNG6tXrjPBmDFjSEn2smCP8ctmljbZ2FSVxOlnTEq4vra4B4yI2IGngfOAEcBVIjIi3nWYZcKECYgIjgN2ggx7Mwl7M79znK2hHHwNCbeEYmfhdDr58eVXsKLMxa4GY/tiZu/0YLfbE2qx7xZmtGBOAAqUUoVKKT/wBnCJCXWYIisri+HDR+Cs3rn/fb6+E/D1nfCd4xxVO7HZbJx44onxLlGL+vGPf4zb5eLtbcYtkbG7wcaXe92cd/4FCbkMqhkB0xsoPuDtkuj7uowzzjgdaahAmmsOfoBSuKp3MGbsWNLS0uJbnLZft27d+K9rruGbcierylvXF9M3JUjflNZNMVAKXs5PxePxcu2113agUusyI2AOdpP5gx5PEblBRFaIyIpEm59x+umngwhJFYUH/bitsQKaa5l0xhnxLUz7gSuvvJJ+ffvwcn4aDYEj94/8bGgjPxvaugmtX+5xsanKwfU33EhmZuaRX9AJmREwJcCBXeV5wO7vH6SUmq6UGqeUGpednR234uIhOzubkSNH4qzaftCnSUkVhdjtdk455RQTqtMOlJSUxF13T6Xab+Pl/NgNgtvXaGNGQQqjR43ioosuitl5rcaMgFkODBGRASLiBKYAH5hQh6nOnDQJaar+4dMkpXBW7+CEE8aTmppqTnHadwwfPpxrr72WJftcLNrj7PD5gmF4dmMaSS4vU++5J+F2EjhQ3P9nSqkg8GvgE2AT8JZSakO86zDbqaeeGnmaVLXjO++3NZSBr55Jk/TtkZX89Kc/5dhjR/Ly1jR2N3Tsx+atbV4Ka+3c/ts76NGjR4wqtCZTolMp9ZFSaqhSapBS6o9m1GC2zMxMRo0aheuAp0kASZXbsTscnHTSSSZVph2Mw+Hgd7/7PW5vCk9vSMffzg0AVpUnMafYw6WXXtoldohI3LZZJzBx4kRorPr2aZJSOGuKGTNmTMJNeksE2dnZTL3nXorrbcwsaPvXp7LZxnOb0xg8aCA33XSTARVajw4YE5188skAOKojyyRKcy001zIx+n7NesaPH8+UKVOYt8vN8tLW98eEFTy7MZWguPjDfffjchk/QtgKdMCYKDc3l9zcXjhqIw/RHNFtTcaNG2dmWdoR/OIXv2DY0CG8mJ9Kta91Q/s/LnKzpdrBLbfemnDzjQ5HB4zJxo0bS1LDPlBh7HV7ye7Rg969u9S4w07H4XAw9Z578SkHL25JOeK81V0Ndt7ZnszEk0/mRz/6UXyKtAgdMCYbOXIkKujH1lSNs7GMUcfqtV86g379+vGLX1zPqnIn3xxmlK9S8NKWFDzeZG67/faEm8x4JDpgTNayFIOjphjla2DYsGEmV6S11uWXX86A/v2YUZCG7xBPlb7e52RLtYMbbvwlGRkZ8S3QAnTAmKx3794kJTlxRKcNDBo0yOSKtNZyOBz87823UNEE83a5f/DxYBje2Z7CkMGDOf/8802o0Hw6YExmt9vJ65OHPTqit1+/fiZXpLXFcccdx9gxY5hdnPyDVszivS7KmoSf/+IX2O3xW37TSnTAWECf6JawSU5nwk56S2TXXHsttb5IoLRQCj4uTmbokMGRRca6KB0wFtAyXDw7O7vLdQImgpEjRzJwQH8W7PHsf19+jYPdDcJlP768S39NdcBYQEurJV2v/dIpiQgXXnQx22vt+1e/W7THhdfj7hLTAQ5HB4wFnHzyyZx88slcccUVZpeitVPL0hqry5MIK1hT5eaE8RPweDxHeGVi06tJW0C/fv344x+75JzPhJGdnc3gQQNZW5nPMZkBqpvRy52iWzCaFjOjRh9HYa2D/JrIwLtj9aBJHTCaFivDhw/HF4JX85Pplp5GTk6O2SWZTt8iaVqMTJw4keuvvx6fz8fRRx/dpZ8etdABo2kx4na7ufrqq80uw1L0LZKmaYbRAaNpmmF0wGiaZhgdMJqmGUYHjKZphtEBo2maYXTAaJpmGB0wmqYZRtSRlkS3ABEpA3Ye8cDOrTtQbnYRWkx0ha9luVLq3CMd1CkCpisQkRVKKb0hUgLQX8tv6VskTdMMowNG0zTD6ICxjulmF6DFjP5aRuk+GE3TDKNbMJqmGUYHjKZphtEBYwEicq6IbBGRAhG5y+x6tPYRkRdEpFRE1ptdi1XogDGZiNiBp4HzgBHAVSIywtyqtHZ6CTji4LOuRAeM+U4ACpRShUopP/AGcInJNWntoJT6Eqg0uw4r0QFjvt5A8QFvl0Tfp2mdng4Y8x1s6Xk9dkBLCDpgzFcC9Dng7Txgt0m1aFpM6YAx33JgiIgMEBEnMAX4wOSaNC0mdMCYTCkVBH4NfAJsAt5SSm0wtyqtPUTkdeBrYJiIlIjIz82uyWx6qoCmaYbRLRhN0wyjA0bTNMPogNE0zTA6YDRNM4wOGE3TDKMDRjskEblHRDaIyFoRWS0i42NwzotjNWNcROpjcR7NOPoxtXZQInIi8DhwulLKJyLdAadS6oijjEXEER3fY3SN9UqpFKOvo7WfbsFoh5JLZO8bH4BSqlwptVtEdkTDBhEZJyJfRP99n4hMF5FPgVdEZKmIHN1yMhH5QkTGisi1IvKUiKRHz2WLftwrIsUikiQig0RkjoisFJGFInJU9JgBIvK1iCwXkQfj/PnQ2kEHjHYonwJ9RCRfRJ4RkdNa8ZqxwCVKqZ8SWXbiJwAikgv0UkqtbDlQKVUDrAFaznsR8IlSKkBk0ezfKKXGArcDz0SP+TvwrFLqeGBvh/+HmuF0wGgHpZSqJxIYNwBlwJsicu0RXvaBUqop+u+3gCuj//4J8PZBjn8TmBz995ToNVKAk4C3RWQ1MI1IawrgZOD16L9fbdN/SDOFw+wCNOtSSoWAL4AvRGQdcA0Q5NtfTO7vvaThgNfuEpEKETmWSIjceJBLfAD8SUQyiYTZPCAZqFZKjT5UWe3872gm0C0Y7aBEZJiIDDngXaOJ7A++g0gYAFx+hNO8AdwBpCul1n3/g9FW0jIitz6zlFIhpVQtsF1ErozWISIyKvqSxURaOgBXt/1/pcWbDhjtUFKAl0Vko4isJbJe8H3A/cDfRWQhEDrCOf5NJBDeOswxbwI/i/7d4mrg5yKyBtjAt0uI3gz8j4gsB9Lb9t/RzKAfU2uaZhjdgtE0zTA6YDRNM4wOGE3TDKMDRtM0w+iA0TTNMDpgNE0zjA4YTdMM8/8B44XqKTeOfewAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(x='Survived', y='Age', data=train, kind='box')\n",
    "g = sns.factorplot(x='Survived', y='Age', data=train, kind='violin')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.Feature engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 Name/Title "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_title = [i.split(',')[1].split('.')[0].strip() for i in dataset['Name']]\n",
    "dataset['Title'] = pd.Series(dataset_title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.countplot(x='Title', data=dataset)\n",
    "g = plt.setp(g.get_xticklabels(), rotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Title'] = dataset['Title'].replace(['Don', 'Rev', 'Dr', 'Mme', 'Ms',\n",
    "       'Major', 'Lady', 'Sir', 'Mlle', 'Col', 'Capt', 'the Countess',\n",
    "       'Jonkheer', 'Dona'], 'Rare')\n",
    "dataset['Title'] = dataset['Title'].map({'Master':0, 'Miss':1, 'Mrs':2, 'Mr':3, 'Rare':5})\n",
    "dataset['Title'] = dataset['Title'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MY/vRf71j7BJOuz2/cfPYJUhaQRxBSJK6DAhJUpcBIUnqMiAkSV0GhCSpy4CQJHWdtbe5StK0fveX/njsEk67X/ytn/6Oj+EIQpLUZUBIkroMCElSlwEhSeoyICRJXcsqIJK8LclXkhxIcvvY9UjSSrZsAiLJOcDvAdcBbwTenuSN41YlSSvXsgkI4CrgQFU9WVV/CXwE2DRyTZK0Yi2ngFgLHFywPdfaJEkjSFWNXQMASW4E/mFV/Yu2/U7gqqq67bh+W4GtbfMNwFeWtNC+S4Cvj13EMuFvMeHvcIy/xTHL5bf461W1+lSdltNUG3PA+gXb64BDx3eqqm3AtqUqahpJZqtqZuw6lgN/iwl/h2P8LY45036L5XSK6XPAxiSvT3IecBOwc+SaJGnFWjYjiKp6OckvAp8AzgHeX1VfGrksSVqxlk1AAFTVA8ADY9exCMvqlNfI/C0m/B2O8bc45oz6LZbNRWpJ0vKynK5BSJKWEQPiO+DUIBNJ3p/kSJLHxq5lbEnWJ3kwyf4kX0ryrrFrGkuS85N8NskX2m/xK2PXNKYkX03yxSR7k8yOXc80PMW0SG1qkP8N/BSTW3Q/B7y9qh4ftbARJHkz8CKwo6p+ZOx6xpRkDbCmqj6f5EJgD3DDCv3vIsAFVfViktcCnwHeVVUPj1zaKJJ8FZipquXwHMRUHEEsnlODNFX1aeCZsetYDqrqcFV9vq1/E9jPCp0RoCZebJuvbR//RXoGMSAWz6lBdFJJNgBXAI+MW8l4kpyTZC9wBNhVVSv2t2ASjn+SZE+bEWLZW1a3uZ5h0mnzX0cCIMn3Ah8F3l1VL4xdz1iq6tvA5UlWAfcl+ZGqWqnXqq6pqkNJLgV2JflyG30vW44gFm+qqUG08rTz7R8FPlRVfzR2PctBVT0HPAS8beRSRlNVh9ryCHAfk9PUy5oBsXhODaITtAuzdwP7q+q3x65nTElWt5EDSb4b+Engy+NWNY4kF7SbFkhyAfBWYNmPpAyIRaqql4GjU4PsB+5ZqVODJPkw8L+ANySZS7Jl7JpGdA3wTuAt7XbGvUmuH7uokawBHkyyj8k/qHZV1cdGrmkslwGfSfIF4LPA/6iqj49c0yl5m6skqcsRhCSpy4CQJHUZEJKkLgNCktRlQEiSugwI6VVI8roFt69+Lcn/WbD9Z63PhiTvWPCdH0+yUm/v1BnMqTakV6GqvgFcDpDkl4EXq+o3j+u2AXgH8AdLWpx0mjmCkE6TJEdnLn0P8A/aqOJfHtfngvb+jM8leTTJipwBWGcGA0I6/W4H/mdVXV5Vdxy37z8An6yqvwv8BPAbbeoFadkxIKSl9Vbg9jYF9kPA+cAPjlqR9Aq8BiEtrQD/pKq+MnYh0qk4gpBOv28CF77Cvk8At7VZX0lyxZJVJb1KBoR0+u0DXk7yheMvUgO/yuTVm/uSPNa2pWXJ2VwlSV2OICRJXQaEJKnLgJAkdRkQkqQuA0KS1GVASJK6DAhJUpcBIUnq+v8sWdvlnnsM7QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.countplot(dataset['Title'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(x='Title', y='Survived', data=dataset, kind='bar')\n",
    "g = g.set_xticklabels(['Master', 'Miss', 'Mrs', 'Mr', 'Rare'])\n",
    "g = g.set_ylabels('survival probability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.589744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.706215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.792000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.157895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.444444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Survived\n",
       "Title          \n",
       "0      0.589744\n",
       "1      0.706215\n",
       "2      0.792000\n",
       "3      0.157895\n",
       "5      0.444444"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[['Title', 'Survived']].groupby('Title').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.drop(labels=['Name'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Family size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Fsize'] = dataset['SibSp'] + dataset['Parch'] + 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(x='Fsize', y='Survived', data=dataset, kind='bar')\n",
    "g = g.set_ylabels('Survival probability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.get_dummies(dataset, columns=['Title'])\n",
    "dataset = pd.get_dummies(dataset, columns=['Embarked'], prefix='Em')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3 Cabin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                 292\n",
       "unique                186\n",
       "top       B57 B59 B63 B66\n",
       "freq                    5\n",
       "Name: Cabin, dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Cabin'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      C85\n",
       "3     C123\n",
       "6      E46\n",
       "10      G6\n",
       "11    C103\n",
       "Name: Cabin, dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Cabin'][dataset['Cabin'].notnull()].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Cabin'] = pd.Series([i[0] if not pd.isnull(i) else 'X' for i in dataset['Cabin']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['X', 'C', 'E', 'G', 'D', 'A', 'B', 'F', 'T'], dtype=object)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Cabin'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.countplot(dataset['Cabin'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(x='Cabin', y='Survived', data=dataset, kind='bar')\n",
    "g = g.set_ylabels('Survival probability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.get_dummies(dataset, columns=['Cabin'], prefix='Cabin')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.4 Ticket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fsize</th>\n",
       "      <th>Title_0</th>\n",
       "      <th>Title_1</th>\n",
       "      <th>Title_2</th>\n",
       "      <th>Title_3</th>\n",
       "      <th>Title_5</th>\n",
       "      <th>Em_C</th>\n",
       "      <th>Em_Q</th>\n",
       "      <th>Em_S</th>\n",
       "      <th>Cabin_A</th>\n",
       "      <th>Cabin_B</th>\n",
       "      <th>Cabin_C</th>\n",
       "      <th>Cabin_D</th>\n",
       "      <th>Cabin_E</th>\n",
       "      <th>Cabin_F</th>\n",
       "      <th>Cabin_G</th>\n",
       "      <th>Cabin_T</th>\n",
       "      <th>Cabin_X</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>1.981001</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>4.266662</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>2.070022</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>3.972177</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>113803</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>2.085672</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>373450</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age      Fare  Parch  PassengerId  Pclass     Sex  SibSp  Survived  \\\n",
       "0  22.0  1.981001      0            1       3    male      1       0.0   \n",
       "1  38.0  4.266662      0            2       1  female      1       1.0   \n",
       "2  26.0  2.070022      0            3       3  female      0       1.0   \n",
       "3  35.0  3.972177      0            4       1  female      1       1.0   \n",
       "4  35.0  2.085672      0            5       3    male      0       0.0   \n",
       "\n",
       "             Ticket  Fsize  Title_0  Title_1  Title_2  Title_3  Title_5  Em_C  \\\n",
       "0         A/5 21171      2        0        0        0        1        0     0   \n",
       "1          PC 17599      2        0        0        1        0        0     1   \n",
       "2  STON/O2. 3101282      1        0        1        0        0        0     0   \n",
       "3            113803      2        0        0        1        0        0     0   \n",
       "4            373450      1        0        0        0        1        0     0   \n",
       "\n",
       "   Em_Q  Em_S  Cabin_A  Cabin_B  Cabin_C  Cabin_D  Cabin_E  Cabin_F  Cabin_G  \\\n",
       "0     0     1        0        0        0        0        0        0        0   \n",
       "1     0     0        0        0        1        0        0        0        0   \n",
       "2     0     1        0        0        0        0        0        0        0   \n",
       "3     0     1        0        0        1        0        0        0        0   \n",
       "4     0     1        0        0        0        0        0        0        0   \n",
       "\n",
       "   Cabin_T  Cabin_X  \n",
       "0        0        1  \n",
       "1        0        0  \n",
       "2        0        1  \n",
       "3        0        0  \n",
       "4        0        1  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Pclass'] = dataset['Pclass'].astype('category')\n",
    "dataset = pd.get_dummies(dataset, columns=['Pclass'], prefix='Pc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.drop(labels=['PassengerId'], axis=1, inplace=True)\n",
    "dataset.drop(labels=['Ticket'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "dataset['Sex'] = dataset['Sex'].map({'male':1, 'female':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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      "text/plain": [
       "    Age      Fare  Parch  Sex  SibSp  Survived  Fsize  Title_0  Title_1  \\\n",
       "0  22.0  1.981001      0    1      1       0.0      2        0        0   \n",
       "1  38.0  4.266662      0    0      1       1.0      2        0        0   \n",
       "2  26.0  2.070022      0    0      0       1.0      1        0        1   \n",
       "3  35.0  3.972177      0    0      1       1.0      2        0        0   \n",
       "4  35.0  2.085672      0    1      0       0.0      1        0        0   \n",
       "\n",
       "   Title_2  Title_3  Title_5  Em_C  Em_Q  Em_S  Cabin_A  Cabin_B  Cabin_C  \\\n",
       "0        0        1        0     0     0     1        0        0        0   \n",
       "1        1        0        0     1     0     0        0        0        1   \n",
       "2        0        0        0     0     0     1        0        0        0   \n",
       "3        1        0        0     0     0     1        0        0        1   \n",
       "4        0        1        0     0     0     1        0        0        0   \n",
       "\n",
       "   Cabin_D  Cabin_E  Cabin_F  Cabin_G  Cabin_T  Cabin_X  Pc_1  Pc_2  Pc_3  \n",
       "0        0        0        0        0        0        1     0     0     1  \n",
       "1        0        0        0        0        0        0     1     0     0  \n",
       "2        0        0        0        0        0        1     0     0     1  \n",
       "3        0        0        0        0        0        0     1     0     0  \n",
       "4        0        0        0        0        0        1     0     0     1  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 填充 Age 缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "256 1043\n"
     ]
    }
   ],
   "source": [
    "age_nan_index = dataset[dataset['Age'].isnull()].index\n",
    "age_index = dataset[dataset['Age'].notnull()].index\n",
    "# columns = dataset.columns\n",
    "# age_index[250:]\n",
    "# columns\n",
    "# dataset.loc[age_nan_index, ['Age']]\n",
    "print(len(age_nan_index), len(age_index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_test = dataset.loc[age_nan_index, :]\n",
    "age_test.drop(['Age'], axis=1, inplace=True)\n",
    "age_train = dataset.loc[age_index, :]\n",
    "age_train_y = age_train['Age']\n",
    "age_train.drop(['Age'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_train.drop(['Survived'], axis=1, inplace=True)\n",
    "age_test.drop(['Survived'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1043 256\n",
      "1043\n"
     ]
    }
   ],
   "source": [
    "print(len(age_train), len(age_test))\n",
    "print(len(age_train_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.085672</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Fare  Parch  Sex  SibSp  Fsize  Title_0  Title_1  Title_2  Title_3  \\\n",
       "0  1.981001      0    1      1      2        0        0        0        1   \n",
       "1  4.266662      0    0      1      2        0        0        1        0   \n",
       "2  2.070022      0    0      0      1        0        1        0        0   \n",
       "3  3.972177      0    0      1      2        0        0        1        0   \n",
       "4  2.085672      0    1      0      1        0        0        0        1   \n",
       "\n",
       "   Title_5  Em_C  Em_Q  Em_S  Cabin_A  Cabin_B  Cabin_C  Cabin_D  Cabin_E  \\\n",
       "0        0     0     0     1        0        0        0        0        0   \n",
       "1        0     1     0     0        0        0        1        0        0   \n",
       "2        0     0     0     1        0        0        0        0        0   \n",
       "3        0     0     0     1        0        0        1        0        0   \n",
       "4        0     0     0     1        0        0        0        0        0   \n",
       "\n",
       "   Cabin_F  Cabin_G  Cabin_T  Cabin_X  Pc_1  Pc_2  Pc_3  \n",
       "0        0        0        0        1     0     0     1  \n",
       "1        0        0        0        0     1     0     0  \n",
       "2        0        0        0        1     0     0     1  \n",
       "3        0        0        0        0     1     0     0  \n",
       "4        0        0        0        1     0     0     1  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.26675512, 0.20770034, 0.2477424 ])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "kfold = StratifiedKFold(n_splits=10)\n",
    "# # clf = RandomForestClassifier(random_state=42)\n",
    "rg = SVR()\n",
    "res = cross_val_score(rg, age_train, y=age_train_y)\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',\n",
       "  kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rg = SVR()\n",
    "rg.fit(age_train, age_train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_pred = rg.predict(age_test)\n",
    "# age_test.isnull().sum()\n",
    "age_pred = pd.Series(age_pred, index=age_nan_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "dataset.loc[age_nan_index]['Age'] = age_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Fsize</th>\n",
       "      <th>Title_0</th>\n",
       "      <th>Title_1</th>\n",
       "      <th>Title_2</th>\n",
       "      <th>Title_3</th>\n",
       "      <th>Title_5</th>\n",
       "      <th>Em_C</th>\n",
       "      <th>Em_Q</th>\n",
       "      <th>Em_S</th>\n",
       "      <th>Cabin_A</th>\n",
       "      <th>Cabin_B</th>\n",
       "      <th>Cabin_C</th>\n",
       "      <th>Cabin_D</th>\n",
       "      <th>Cabin_E</th>\n",
       "      <th>Cabin_F</th>\n",
       "      <th>Cabin_G</th>\n",
       "      <th>Cabin_T</th>\n",
       "      <th>Cabin_X</th>\n",
       "      <th>Pc_1</th>\n",
       "      <th>Pc_2</th>\n",
       "      <th>Pc_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>4.266662</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>2.070022</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>3.972177</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>2.085672</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age      Fare  Parch  Sex  SibSp  Survived  Fsize  Title_0  Title_1  \\\n",
       "0  22.0  1.981001      0    1      1       0.0      2        0        0   \n",
       "1  38.0  4.266662      0    0      1       1.0      2        0        0   \n",
       "2  26.0  2.070022      0    0      0       1.0      1        0        1   \n",
       "3  35.0  3.972177      0    0      1       1.0      2        0        0   \n",
       "4  35.0  2.085672      0    1      0       0.0      1        0        0   \n",
       "\n",
       "   Title_2  Title_3  Title_5  Em_C  Em_Q  Em_S  Cabin_A  Cabin_B  Cabin_C  \\\n",
       "0        0        1        0     0     0     1        0        0        0   \n",
       "1        1        0        0     1     0     0        0        0        1   \n",
       "2        0        0        0     0     0     1        0        0        0   \n",
       "3        1        0        0     0     0     1        0        0        1   \n",
       "4        0        1        0     0     0     1        0        0        0   \n",
       "\n",
       "   Cabin_D  Cabin_E  Cabin_F  Cabin_G  Cabin_T  Cabin_X  Pc_1  Pc_2  Pc_3  \n",
       "0        0        0        0        0        0        1     0     0     1  \n",
       "1        0        0        0        0        0        0     1     0     0  \n",
       "2        0        0        0        0        0        1     0     0     1  \n",
       "3        0        0        0        0        0        0     1     0     0  \n",
       "4        0        0        0        0        0        1     0     0     1  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copy = dataset.copy()\n",
    "copy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "copy.loc[age_nan_index, 'Age'] = age_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Age'] = copy['Age']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [],
   "source": [
    "copy = train.copy()\n",
    "train = (copy - copy.mean()) / copy.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3694: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  errors=errors)\n"
     ]
    }
   ],
   "source": [
    "# Separate train and test dataset \n",
    "train = dataset[:train_len]\n",
    "test = dataset[train_len:]\n",
    "test.drop(labels=['Survived'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "train['Survived'] = train['Survived'].astype(int)\n",
    "Y_train  = train['Survived']\n",
    "X_train = train.drop(labels=['Survived'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1 Simple modeling "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.1.1 Cross validate models \n",
    "Compared 10 popular classifiers and evaluate the mean accuracy of each of them by a stratified kfold cross validation procedure \n",
    "* SVC\n",
    "* Decision \n",
    "* AdaBoost \n",
    "* RandomForest \n",
    "* Extra Trees \n",
    "* Gradient Boosting \n",
    "* Multiple layer perceprton(neural network)\n",
    "* KNN \n",
    "* Logistic Regression \n",
    "* Linear Discriminant Analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "kfold = StratifiedKFold(n_splits=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# clf = GradientBoostingClassifier(random_state=random_state)\n",
    "# cross_val_score(clf, X_train, Y_train, scoring='accuracy', cv=kfold, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Cross validation scores')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "random_state = 42 \n",
    "classifiers = []\n",
    "classifiers.append(SVC(random_state=random_state))\n",
    "classifiers.append(DecisionTreeClassifier(random_state=random_state))\n",
    "classifiers.append(AdaBoostClassifier(DecisionTreeClassifier(random_state=random_state), random_state=random_state, learning_rate=0.1))\n",
    "classifiers.append(RandomForestClassifier(random_state=random_state))\n",
    "classifiers.append(ExtraTreesClassifier(random_state=random_state))\n",
    "classifiers.append(GradientBoostingClassifier(random_state=random_state))\n",
    "classifiers.append(MLPClassifier(random_state=random_state))\n",
    "classifiers.append(KNeighborsClassifier())\n",
    "classifiers.append(LogisticRegression(random_state=random_state))\n",
    "classifiers.append(LinearDiscriminantAnalysis())\n",
    "\n",
    "cv_results = []\n",
    "for classifier in classifiers:\n",
    "    cv_results.append(cross_val_score(classifier, X_train, Y_train, scoring='accuracy', cv=kfold, n_jobs=4))\n",
    "    \n",
    "cv_means = []\n",
    "cv_std = []\n",
    "for cv_result in cv_results:\n",
    "    cv_means.append(cv_result.mean())\n",
    "    cv_std.append(cv_result.std())\n",
    "\n",
    "cv_res = pd.DataFrame({'CrossValMeans':cv_means, 'CrossValerrors': cv_std,\n",
    "                      'Algorithm':['SVC', 'DecisionTree', 'AdaBoost', 'RandomForest', 'ExtraTrees',\n",
    "                                  'GradientBoosting', 'MultipleLayerPerceptron', 'KNeighboors', \n",
    "                                  'LogisticRegression', 'LinearDiscriminantAnalysis']})\n",
    "g = sns.barplot('CrossValMeans', 'Algorithm', data=cv_res, palette='Set3', orient='h', **{'xerr':cv_std})\n",
    "g.set_xlabel('Mean Accuracy')\n",
    "g.set_title('Cross validation scores')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CrossValMeans</th>\n",
       "      <th>CrossValerrors</th>\n",
       "      <th>Algorithm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.818399</td>\n",
       "      <td>0.043368</td>\n",
       "      <td>SVC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.783235</td>\n",
       "      <td>0.043349</td>\n",
       "      <td>DecisionTree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.797995</td>\n",
       "      <td>0.041619</td>\n",
       "      <td>AdaBoost</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.807048</td>\n",
       "      <td>0.026803</td>\n",
       "      <td>RandomForest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.796821</td>\n",
       "      <td>0.038852</td>\n",
       "      <td>ExtraTrees</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.832035</td>\n",
       "      <td>0.042941</td>\n",
       "      <td>GradientBoosting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.829763</td>\n",
       "      <td>0.040220</td>\n",
       "      <td>MultipleLayerPerceptron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.794586</td>\n",
       "      <td>0.034389</td>\n",
       "      <td>KNeighboors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.824081</td>\n",
       "      <td>0.034833</td>\n",
       "      <td>LogisticRegression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.826341</td>\n",
       "      <td>0.034445</td>\n",
       "      <td>LinearDiscriminantAnalysis</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CrossValMeans  CrossValerrors                   Algorithm\n",
       "0       0.818399        0.043368                         SVC\n",
       "1       0.783235        0.043349                DecisionTree\n",
       "2       0.797995        0.041619                    AdaBoost\n",
       "3       0.807048        0.026803                RandomForest\n",
       "4       0.796821        0.038852                  ExtraTrees\n",
       "5       0.832035        0.042941            GradientBoosting\n",
       "6       0.829763        0.040220     MultipleLayerPerceptron\n",
       "7       0.794586        0.034389                 KNeighboors\n",
       "8       0.824081        0.034833          LogisticRegression\n",
       "9       0.826341        0.034445  LinearDiscriminantAnalysis"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.1.2 Hyperparameter tunning for best models \n",
    "Grid search optimization for SVC, RandomForest, GradientBoosting, multipleLayerPerceptron, LogisticRegression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 54 candidates, totalling 540 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:   26.9s\n",
      "[Parallel(n_jobs=-1)]: Done 196 tasks      | elapsed:  1.4min\n",
      "[Parallel(n_jobs=-1)]: Done 446 tasks      | elapsed:  3.1min\n",
      "[Parallel(n_jobs=-1)]: Done 540 out of 540 | elapsed:  3.8min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8354143019296254"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RandomForest\n",
    "RFC = RandomForestClassifier()\n",
    "\n",
    "rf_param_grid = {\n",
    "    'max_depth': [None],\n",
    "    'max_features': [1,3,10],\n",
    "    'min_samples_split': [2,3,10],\n",
    "    'min_samples_leaf':[1,3,10],\n",
    "    'bootstrap': [False],\n",
    "    'n_estimators': [100, 300],\n",
    "    'criterion': ['gini']\n",
    "}\n",
    "\n",
    "gsRFC = GridSearchCV(RFC, param_grid=rf_param_grid, cv=kfold, \n",
    "                     scoring='accuracy', n_jobs=-1, verbose=1)\n",
    "gsRFC.fit(X_train, Y_train)\n",
    "RFC_best = gsRFC.best_estimator_\n",
    "gsRFC.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train['Age'] = X_train['Age'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 72 candidates, totalling 720 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:    7.3s\n",
      "[Parallel(n_jobs=-1)]: Done 196 tasks      | elapsed:   27.6s\n",
      "[Parallel(n_jobs=-1)]: Done 446 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done 720 out of 720 | elapsed:  1.7min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8229284903518729"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# GradientBoosting \n",
    "GBC = GradientBoostingClassifier()\n",
    "gb_param_grid = {\n",
    "    'loss': ['deviance'],\n",
    "    'n_estimators': [100, 200, 300],\n",
    "    'learning_rate': [0.1, 0.05, 0.01],\n",
    "    'max_depth':[4,8],\n",
    "    'min_samples_leaf':[100, 150],\n",
    "    'max_features': [0.3, 0.1]\n",
    "}\n",
    "gsGBC = GridSearchCV(GBC, param_grid=gb_param_grid, cv=kfold, scoring='accuracy',\n",
    "                    n_jobs=-1, verbose=1)\n",
    "gsGBC.fit(X_train, Y_train)\n",
    "GBC_best = gsGBC.best_estimator_\n",
    "gsGBC.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 28 candidates, totalling 280 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:   11.1s\n",
      "[Parallel(n_jobs=-1)]: Done 196 tasks      | elapsed:   44.9s\n",
      "[Parallel(n_jobs=-1)]: Done 280 out of 280 | elapsed:  1.6min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8331441543700341"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# SVC \n",
    "SVMC = SVC(probability=True)\n",
    "svc_param_grid = {\n",
    "    'kernel': ['rbf'],\n",
    "    'gamma': [0.001, 0.01, 0.1, 1],\n",
    "    'C': [1, 10, 50, 100, 200, 300, 1000]\n",
    "}\n",
    "gsSVMC = GridSearchCV(SVMC, param_grid=svc_param_grid, cv=kfold,\n",
    "                     scoring='accuracy', n_jobs=-1, verbose=True)\n",
    "gsSVMC.fit(X_train, Y_train)\n",
    "SVMC_best = gsSVMC.best_estimator_\n",
    "gsSVMC.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.1.3 Plot learn curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n",
    "                       n_jobs=-1, train_sizes=np.linspace(.1, 1, 5)):\n",
    "    \"\"\"Generate a simple plot os the best and training learning curve\"\"\"\n",
    "    plt.figure()\n",
    "    plt.title(title)\n",
    "    if ylim is not None:\n",
    "        plt.ylim(*ylim)\n",
    "    plt.xlabel('Training examples')\n",
    "    plt.ylabel('Score')\n",
    "    train_sizes, train_scores, test_scores = learning_curve(estimator, X, y, cv=cv,\n",
    "                                                           n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    plt.grid()\n",
    "    \n",
    "    plt.fill_between(train_sizes, train_scores_mean-train_scores_std, \n",
    "                    train_scores_mean+ train_scores_std, alpha=0.1, color='r')\n",
    "    plt.fill_between(train_sizes, test_scores_mean-test_scores_std,\n",
    "                    test_scores_mean + test_scores_std, alpha=0.1, color='g')\n",
    "    plt.plot(train_sizes, train_scores_mean, 'o-', color='r', label='Training Score')\n",
    "    plt.plot(train_sizes, test_scores_mean, 'o-', color='g', label='Cross-validation score')\n",
    "    plt.legend(loc='best')\n",
    "    return plt \n",
    "\n",
    "\n",
    "g = plot_learning_curve(gsRFC.best_estimator_, 'RF learning curves', X_train, Y_train, cv=kfold)\n",
    "g = plot_learning_curve(gsSVMC.best_estimator_, 'SVC learning curves', X_train, Y_train, cv=kfold)\n",
    "g = plot_learning_curve(gsGBC.best_estimator_, 'GradientBoosting learning curves', \n",
    "                       X_train, Y_train, cv=kfold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.1.4 Feature importance of tree based classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2mi8gOxXe6ohdRPyO7CzM9Wn7T5C9ZrXyB7LrmR8ju0HsijT/SrIcfT/Za/d3Wg58tFDh/fIdspvL3k59/7baoCLiYbJvhLgorX8fH408HkM20PAU2fv/N7S8ZKHmmi9iNbMeQNJ4YPOIOKresZhZ51H2ZdKfj4guHRnqjiQFsEVEzK53LI3GI31mZmbdjKR/kLRzOg24Fdkp59/VOy5rbMX7NmozM7Purw/ZKfXNyK5jvp7s1LLZKvPpXTMzM7MC8OldMzMzswJw0WdmZmZWAL6mr4zBgwfH8OHD6x2GmXWRWbNmzY+IIfWOozM4f5kVT7U5zEVfGcOHD2fmzJn1DsPMuoikF9tu1Ricv8yKp9oc5qKvjOVvvMkbl02qdxhmtoqGnFjcryl0/jKrvUbNMb6mz8zMzKwAXPSZmZmZFYCLPjMzM7MCcNFnZmZmVgAu+szMuoCk4ZLekjQtPc6sd0xmVix1vXtX0kDgD+npDsDTwPvAYGAPYBlwUERcndqPB2ZHRNW3pkn6L2A00Be4LyKcaM2sXmZFxJhqG0taLSJW1DIgMyuOuhZ9EfE20AQgaRpwVES83Lxc0nDgGODqDmzmWxGxNPV3n6RtIuLJDvRnZtYpJN0BrAGsBZwSETPSwe1wYBDwK0mvAt8FAvgrcGL4n6ab2Srolqd306mPocBpwMj0fP+SNodLekDSdEnnVuorV/D1Bt4FXq1l7GZmrRiZO727O/DZiGgCvgicl2v3fkQcBFwPXEx2xqMJWALsj5nZKujuX858ITCi+XSIpB3Tz3WB04FdI2KZpN9J2i4iHi/XiaRLgYOBO4G3K7QZB4wDGDpovU7fETMzcqd3JfUFLpW0FbAC2DjX7qH0czDZqN8fJAH0A/5W2qnzl5lVo1uO9FVhc2BT4K50Wniz9LysiDg5tRkMjK3QZmJEjIqIUev1G9D5EZuZtTQWWBERuwInAcota76Obz7wHHBARDRFxCjgitKOnL/MrBrdfaRvKeVjfA6YDYyJiOWSetEyYX5I0poR8ffU7l3gvdqFa2ZWtRnANyXdDTxYrkFEhKTTgJuUDfV9AHwN+EvXhWlmPUV3L/rmAkskTQYmNM+MiAWSLgamSlpBdpfvMal9qWslrQf0BqZHxLTah21m1lJEvACMyT2fS/bNAs2+neaPL1nvPmDP2kdoZj1dtyn60kXKK00D++am78m1mQxMrqLfQzshPDMzM7OG1m2Kvo6SNILcaGAyMSKuq0c8ZmZmZt1Jjyn6IuIp0nf+mZmZmVlLjXr3rpmZmZm1Q48Z6etMqw8ZxJATj6p3GGZm7eb8ZWaVeKTPzMzMrABc9JmZmZkVgIs+MzMzswLwNX1lLH9jHq//7MJ6h2ENav0TTqt3CFZgzl9WJM637eORPjMzM7MCcNFnZmZmVgAu+szMzMwKwEWfmZmZWQE05I0cknoBlwHbAh8AL0XEkfWNysysepImAltGRFO9YzGzYmjIog8YC6weETsDSBpU53jMzKomqQ+wPfC6pE0i4qV6x2RmPV+jnt59B9hW0scBIuJNSQMl3SDpHklTJW0uaQ1J0yVtLWkDSQ9LWqfOsZuZ7Q/cBPwSOAJA0o8lzZD0M0kvpnm9Jf1C0r0pl42uY8xm1uAacqQvIu6X9HNggqThwCXAhsBvI+J6SdsDF0TEYZKOA64C3gZOjYiFdQrbzKzZF4AzgHnAnZLuBLaJiJ0kbQp8KbX7EjA7Io6XtAHwW2DnukRsZg2vIYs+gIi4ErhS0gDgfuA1YHdJJ6Qmy1O7ZyQ9DwyKiIcq9SdpHDAOYOigdWsau5kVl6SBZIXbxDRrOHAg8AhARLwoaV5ath3waUlj0/OBFfp0/jKzNjVk0SdpI+CdiFgELCY73fsOMDEifpfa9Ek/9wJ6A/MlHRQRN5XrMyImkpLwDpsOi9rvhZkV1GHADyLiJwCS/gU4C1iWnm8CbJDaPkk20ndRWtanXIfOX2ZWjYYs+oChwEWSPiDbhynAT4GfSToZEHCzpGuA84B9yEb+7pb0p4h4uU5xm5kdSRqVS6YDw8hO884AngBeScsuBy6VdG96PhM4s6sCNbOepSGLvoh4mPLXtXyhzLz8hc+fqk1EZmbViYg9S56/D2wtqXdELEvX9O2Yli0DTijTjZlZuzVk0Wdm1gNdLGlboB/ZTR5mZp3KRZ+ZWTcQEV+pdwxm1rM16vf0mZmZmVk7uOgzMzMzKwCf3i1j9SEbsP4Jp9U7DDOzdnP+MrNKPNJnZmZmVgAu+szMzMwKwEWfmZmZWQH4mr4ylr7+Ai//5Lh6h9Ewhn71ynqHYGaJ81d5zlNmHukzMzMzKwQXfWZmZmYF4KLPzMzMrABc9JmZmZkVQMMUfZKGS3pL0rT0OLPeMZmZmZk1ika7e3dWRIyptrGk1SJiRS0DMrPikjQQ+EN6ugPwNPA+MBjYA1gGHBQRV6f244HZETGpHdv4L2A00Be4LyJ8wGtmq6TRir4WJN0BrAGsBZwSETNSUh0ODAJ+JelV4LtAAH8FToyIqE/EZtaTRMTbQBOApGnAURHxcvNyScOBY4CrO7CZb0XE0tTffZK2iYgnO9CfmRVUw5zeTUbmTu/uDnw2IpqALwLn5dq9HxEHAdcDF5MdaTcBS4D9y3UsaZykmZJmvvnO32u6E2bWs6UcNRQ4jY/y1v4lbQ6X9ICk6ZLOrdRXruDrDbwLvFpme85fZtamRhvp+/D0rqS+wKWStgJWABvn2j2Ufg4mG/X7gySAfsDfynUcEROBiQCf2GSwRwLNrDNcCIzI5a0d0891gdOBXSNimaTfSdouIh4v14mkS4GDgTuBt0uXO3+ZWTUabaQvbyywIiJ2BU4ClFvWfB3ffOA54ICIaIqIUcAVXRummdlKNgc2Be5Kp4U3S8/LioiTU5vBZLnPzKzdGm2kL28G8E1JdwMPlmsQESHpNOAmZUN9HwBfA/7SdWGaWYEtpXyefQ6YDYyJiOWSetHywPVDktaMiL+ndu8C79UuXDPryRqm6IuIF4Axuedzye5oa/btNH98yXr3AXvWPkIzs5XMBZZImgxMaJ4ZEQskXQxMlbSC7C7fY1L7UtdKWg/oDUyPiGm1D9vMeqKGKfrMzLqzdLPYStPAvrnpe3JtJgOTq+j30E4Iz8zMRZ+ZWXcgaQS50cBkYkRcV494zKzncdFnZtYNRMRTpO/8MzOrhUa+e9fMzMzMquSRvjL6rD+coV+9st5hmJm1m/OXmVXikT4zMzOzAnDRZ2ZmZlYALvrMzMzMCsDX9JXx3huzeeyyA+sdRs3scOKUeodgZjXS0/NXeznfmX3EI31mZmZmBeCiz8zMzKwAXPSZmZmZFYCLPjMzM7MCaNgbOSRNBLYs+cfmZmbdnqRewGXAtsAHwEsRcWR9ozKznq4hiz5JfYDtgdclbRIRL9U7JjOzdhgLrB4ROwNIGlTneMysABr19O7+wE3AL4EjACT9WNIMST+T9GKa11vSLyTdK2m6pNF1jNnMrNk7wLaSPg4QEW9KGijpBkn3SJoqaXNJa6TctbWkDSQ9LGmdOsduZg2qIUf6gC8AZwDzgDsl3QlsExE7SdoU+FJq9yVgdkQcL2kD4LfAzuU6lDQOGAfwD4P61jp+MyuwiLhf0s+BCZKGA5cAGwK/jYjrJW0PXBARh0k6DrgKeBs4NSIWlvbn/GVm1Wi4ok/SQLLCbWKaNRw4EHgEICJelDQvLdsO+LSksen5wEr9RsTE5j5HbLpOdH7kZmYfiYgrgSslDQDuB14Ddpd0QmqyPLV7RtLzwKCIeKhCX85fZtamhiv6gMOAH0TETwAk/QtwFrAsPd8E2CC1fZJspO+itKxP14drZtaSpI2AdyJiEbCY7HTvO8DEiPhdatMn/dwL6A3Ml3RQRNxUp7DNrME1YtF3JOk0RjIdGEZ2mncG8ATwSlp2OXCppHvT85nAmV0VqJlZBUOBiyR9QJaHpwA/BX4m6WRAwM2SrgHOA/YhG/m7W9KfIuLlOsVtZg2s4Yq+iNiz5Pn7wNaSekfEsnRN345p2TLghDLdmJnVTUQ8TPnri79QZl7+BrRP1SYiMyuChiv6WnGxpG2BfmQ3eZiZmZlZ0mOKvoj4Sr1jMDMzM+uuGvV7+szMzMysHVz0mZmZmRVAjzm925nWGrI5O5w4pd5hmJm1m/OXmVXikT4zMzOzAnDRZ2ZmZlYALvrMzMzMCsDX9JWxeP6z3PuL/esdRoftcfwt9Q7BzLpYT8lfHeX8Z7Yyj/SZmZmZFYCLPjMzM7MCcNFnZmZmVgAu+szMzMwKwEWfmZmZWQE0TNEnabiktyRNkzRT0hFVrrelpIfSeg9K2r7WsZqZ5Tl/mVl30Ghf2TIrIsZIGgD8RdINEbG8jXWeA3aOiJC0J3AOcHjNIzUza8n5y8zqqmFG+vIiYhEwFzg0Hf1Ok3RRhbbLIyLS0wHAX8q1kzQuHYHPfHvx0toEbmaF5/xlZvXSaCN9AEjaGBgCfA/YNSLmSVqtlfYjgZ8AmwCfLdcmIiYCEwG2Gj4wyrUxM+so5y8zq5dGG+kbKele4BrgbOCNiJgHEBErKq0UEbMiYifgEODSLonUzKwl5y8zq6tGK/pmRcQeEbEncAMwSNIQAEll90XSmrmnC4H3ah+mmdlKnL/MrK4a8vQuQLqw+SvATZLeBx4Fvlam6b9I+gbQfCR9alfFaGZWjvOXmdVDwxR9EfECMKZk3lRgpzbWuwXwf942s7px/jKz7qBhir5qSPohMDo3a2lE7F2veMzMquX8ZWa11qOKvoj4er1jMDNbFc5fZlZrjXYjh5mZmZmtgh410tdZ+g/egj2O92U0ZtZ4nL/MrBKP9JmZmZkVgIs+MzMzswJw0WdmZmZWAL6mr4yF85/ld1fuW+8wqnLIcbfVOwQz60a6S/5ybjLrfjzSZ2ZmZlYALvrMzMzMCsBFn5mZmVkBuOgzMzMzK4BuX/RJGi7pLUnT0uPMMm3GSjq6HvGZmZmZNYJGuXt3VkSMqbQwIm7vymDMzAAkDQT+kJ7uADwNvA8MBvYAlgEHRcTVqf14YHZETGrHNk4CTgV6RcTmnRe9mRVNtx/pKyVpQ0n3S7o3jfwNkHSspHMkbZwbEXxS0uS0zsmSHpA0Q9Lx9d4HM+sZIuLtiGiKiCbgMeDw9HzbiHgDWAc4poObmQxs08E+zMwapugb2VzMAV8GpkfEHmRH0oubG0XEKyn5fgZYAJwr6ePAWGA3YBfgOEnrlW5A0jhJMyXNXPTO0prvkJn1XClfDQVO46P8tX9Jm8PTweh0SedW6isi5kXEsja25/xlZm1qlKJvVu5o+gJgmaRJwPlA73xDSWsA1wPnRMSTwLbACOBe4B5gADCsdAMRMTEiRkXEqAH9+tR0Z8ysMC7ko/x1S/NMSesCpwN7RsQuwCclbbeqG3H+MrNqNMo1fXmrRcS3AST9AtineYEkAf8DXBER96fZTwOPAodGREjq3dZRs5lZjW0ObArclaUt1knPH69nUGbWszVi0dck6WxgOdkF09PJTudCdvp2f2AjSV8lOw18jqS7gfskrQCWSDooIpbXI3gzK5SllM+zzwGzgTERsVxSL0BdGpmZFU63L/oi4gVgTO75rcCtJc2uyk0PLNPHBGBCDcIzM2vNXLIDzcnkclBELJB0MTA1HYwuI7vhY25pB5IOJ7uWeaN0AHtuRDzUJdGbWY/S7Ys+M7NGkK45Xmka2Dc3fU+uzWSyO3Pb6vdG4MaOR2hmReeiz8ysG5A0gpXPSEyMiOvqEY+Z9Twu+szMuoGIeApoqnccZtZzNcpXtpiZmZlZB3ikr4x1Bm/BIcfdVu8wzMzazfnLzCrxSJ+ZmZlZAbjoMzMzMysAF31mZmZmBeBr+spYsOAZrvrl3nXb/rFfvLNu2zazxlbv/NXMecys+/FIn5mZmVkBuOgzMzMzKwAXfWZmZmYF4KLPzMzMrADqWvRJGihpWnoslDQjTT8haYikdSQdk2s/XtJR7dzGSZKekTS78/fAzKwyScMlvZXLc2eWaTNW0tH1iM/MiqWud+9GxNuk/zUpaRpwVES83Lxc0nDgGODqDmxmMnA58HQH+jAzW1WzImJMpYURcXtXBmPKdBa4AAAgAElEQVRmxdUtT++mI+KhwGnAyPR8/5I2h0t6QNJ0SedW6isi5kXEslrHbGbWFkkbSrpf0r0prw2QdKykcyRtnBsRfFLS5LTOySnXzZB0fL33wcwaV3f/nr4LgRHNR8mSdkw/1wVOB3aNiGWSfidpu4h4fFU3JGkcMA5gvfXW7HjkZmaZkelMBsC9wPSIOFuS8o0i4hWgSdJAYApwrqSPA2OB3cgO0h+Q9LuIWJBf1/nLzKrRLUf6qrA5sClwV0qmm6XnqywiJkbEqIgY1b9/704I0cwMyE7vNkVEE3ABsEzSJOB8oEWykbQGcD1wTkQ8CWwLjCArFu8BBgDDSjfg/GVm1ejuI31LKR/jc8BsYExELJfUC1CZdmZm3clqEfFtAEm/APZpXpBG/v4HuCIi7k+znwYeBQ6NiJDU25ermNmq6u4jfXOBJZImS/qX5pnp1MbFwFRJ9wK3A0PKdZCu/bsb2EjS3ZI+3RWBm5mV0ZSuQ55GNmI3PbdsF2B/4Kvpur7vR8QTwN3AfSnX/UFSdz9YN7Nuqtskj3TqY6VpYN/c9D25NpPJ7sxtq98bgRs7HqGZWftExAvAmNzzW4FbS5pdlZseWKaPCcCEGoRnZgXTbYq+jpI0gpUT48SIuK4e8ZiZmZl1Jz2m6IuIp0jf+WdmZmZmLXX3a/rMzMzMrBP0mJG+zrTeelty7BfvrHcYZmbt5vxlZpV4pM/MzMysAFz0mZmZmRWAiz4zMzOzAnDRZ2ZmZlYAvpGjjNfffJZLrtun7YY1csoRd9Rt22bW2OqVv5y3zLo/j/SZmZmZFYCLPjMzM7MCcNFnZmZmVgAu+szMzMwKoK5Fn6SBkqalx0JJM9L0E5KGSFpH0jG59uMlHdXObdwg6SFJ/yvp2E7fCTMrBEmfkHRbylEPSTqtQrtjJZ1TZv5ZkrZr5zbvljQ6TY9I211t1fbAzIqurnfvRsTbQBOApGnAURHxcvNyScOBY4CrO7CZb0XEs5LWBJ6QdH1E/L0D/ZlZwUgaAEwCDomI/5MkYO/29BERF6zCpk8CrpG0CzAB+GpErFiFfszMuufp3XQkPRQ4DRiZnu9f0uZwSQ9Imi7p3Ep9RcSzaXIZ8AEQNQvczHqqA4ApEfF/AJG5Q9IvU376k6SDcu23lzRF0qOSdgWQdFUq3pD0oqRLJP1R0o8qbTQingFuBaYCsyLiTzXbQzPr8bpl0ZdzIVmia4qIW5pnSloXOB3YMyJ2AT5ZxWmTs4FfRcT75RZKGidppqSZ7yxe2lnxm1nPMAyYU2b+SRHRBOwFnJ+b3z8iDgQOAS4qs94GwA+AnYAD0khiJbcCuwC/rNTA+cvMqtHdi75KNgc2Be5Kp4U3S8/LStcFbgt8p1KbiJgYEaMiYlS//n06OVwza3BzgE3yMyT1Ar4taTowmZY56BGAiHgBGFimv1ciYm5EBPAysG65jaZtXAh8Fah4etj5y8yq0d2LvqWUv+7wOWA2MCYdZf8TcFu5DiR9BjgCODoiPqhRnGbWs91MNiL3j7l5pwOfAHYFDiO7fKTZSABJmwCLyvRXepmJKmz3RGBGRPwUmCvpc6sQu5kZ0P3/DdtcYImkyWQXMQMQEQskXQxMlbSC7Hq9Y1L7UtcCfwXuzK695siIeKXmkZtZjxERiyQdDfw03RTWh6wQ7A1MAx4DFuZWeU/SLcBGwNdWZZuSNgK+THYKGOBMspx3e0SUKyTNzFrVbYq+NGK30jSwb276nlybyWSnVNrqt18nhGdmBRcRfwbGlszOX8d3Smp3FXBVmfWPzU1vnpseU2F7r5KNJDY/XwBs3+7AzcySblP0dZSkEeRGA5OJEXFdPeIxM6uW85eZdYUeU/RFxFOk7/wzM2skzl9m1hW6+40cZmZmZtYJesxIX2daf9AWnHLEHfUOw8ys3Zy/zKwSj/SZmZmZFYCLPjMzM7MCcNFnZmZmVgAu+szMzMwKwDdylPHKW8/yrRtLv4O1a5x3+O112a6Z9Qxdkb+cp8wak0f6zMzMzArARZ+ZmZlZAbjoMzMzMysAX9NnZtaJJPUCLgO2BT4AXgKuAYZExDWSpgFHRcTLJet9B9gHWAqsiIg9ujRwM+vxurTok/QJ4D+BvkAf4DcRcWGZdscCQyPi+yXzzwJuiYjH27HNu4GzI+Lh9E/NfwHsGhErVn1PzMwqGgusHhE7A0gaFBFvtrZCyk27RMQ/N69T+zDNrGi67PSupAHAJOCrEdEE7Aw82Z4+IuKC9hR8yUnApZJ6AxPS9l3wmVmtvANsK+njABHxpqRjJZ2Ta3OapLsl3SypH7AE2ETSSEm9motESU2S7pA0WdJjkg7v+t0xs56iK6/pOwCYEhH/BxCZOyT9UtI0SX+SdFCu/faSpkh6VNKuAJKukrRLmn5R0iWS/ijpR5U2GhHPALcCU4FZEfGnmu2hmRVeRNwP/ByYIOl5SaeWafZIRIwBZgDHR8TzwBnA94EXJF2Qa7sx8HlgN+C8dPrYzKzdujJ5DAPmlJl/Uhr52ws4Pze/f0QcCBwCXFRmvQ2AHwA7AQekkcRKbgV2AX5ZqYGkcZJmSpr53qKlre6ImVlrIuLKdE3e9sCxrJxrH04//xfYKq3zh4jYF9gC2EFS8zV9j0bEsohYBLwODCndnvOXmVWjK4u+OcAm+RnpiPXbkqYDk4FNc4sfAYiIF4CBZfp7JSLmRkQALwPrltto2saFwFeBC8q1SduZGBGjImLUWgP6VL1TZmZ5kjbKHYQuJjvdq5Jmo9LPHYFnJA2StD5ARLwPvMlH+XkHSatL6k92sDu/dJvOX2ZWja4s+m4mG5H7x9y804FPALsCh5Hd6dZsJICkTYBFZfqLkuelSbXZicCMiPgpMFfS51YhdjOzag0FbpP0APAQ2ZmG0uuId5J0D9kp21+QHdjeKOlBSQ+SFXZTU9tXgRuBB4BzfE2yma2qLrt7NyIWSToa+KmkNcnu3r0Z6A1MAx4DFuZWeU/SLcBGwNdWZZuSNgK+THYKGOBMYKqk29OpEjOzThURD5PdqFZpeVOZ2YuB3UtnSgKYExHHd1Z8ZlZcXfqVLRHxZ7KvM8jLX8d3Smp3FXBVmfWPzU1vnpseU2F7r5KNJDY/X0B2jY2ZmZlZofSYL2dO33M1oWT2xIi4rh7xmJl1VERMIzsTYmbWYT2m6IuIp4CmesdhZmZm1h35+57MzMzMCqDHjPR1po3X3YLzDr+93mGYmbWb85eZVeKRPjMzM7MCcNFnZmZmVgAu+szMzMwKwNf0lfHswmcZe9N+Xba92w+6tcu2ZWY9W63yl/OUWePzSJ+ZmZlZAbjoMzMzMysAF31mZmZmBeCiz8zMzKwAumXRJ6mXpJ9LelDSA5KulTRW0tFp+TRJQ8us9x1Jf5R0v6R7uz5yMys6ScMlvZXy1ExJR3Sgr/GSjurM+MysuLrr3btjgdUjYmcASYMi4s3WVpA0AtglIv65eZ3ah2lmVtasiBgjaQDwF0k3RMTySo0lrRYRK7owPjMroG450ge8A2wr6eMAEfGmpGMlnZNrc5qkuyXdLKkfsATYRNJISb2ai0RJTZLukDRZ0mOSDu/63TGzIoqIRcBc4HJJ90r6k6Rx0CI33QicJ2kTSbdLuk/SPZKa8/Mekm5K+Wvreu2LmTW+bln0RcT9wM+BCZKel3RqmWaPRMQYYAZwfEQ8D5wBfB94QdIFubYbA58HdiNLrt1yv82sZ5G0MTAE+GpE7AHsBJwhqXdqshFwREScBfwIuDAidgf2iogPUpvFEXEQ8EPg+K7dAzPrSbpt8RMRV6YkuT1wLCvH+nD6+b/AVmmdP0TEvsAWwA6S9khtHo2IZemo+3WyJNyCpHHp+puZSxct7fwdMrMiGZmuK74GGAecKGk6cCewfnoAzIyIZWl6G+BegFzBBzAr/XwJWK/cxpy/zKwa3bLok7RRuhYGYDHZ6V6VNBuVfu4IPCNpkKT1ASLifeBNPtq/HSStLqk/sAEwv3SbETExIkZFxKg+A/p08h6ZWcHMiog9ImJP4E/AccDuwD7A23yUz/LX8T0JNEF2M1tufuSmS/Ng1sD5y8yq0F1v5BgKXCTpA7IYp9AyOQLslK6NWQp8DhgMXCWpeZ9mAVPJEu2rwI3AZsA5vmDazLrQQrKCbjrwNLCgQrszyK79OwdYJmnvLorPzAqiWxZ9EfEwsHMry5vKzF5MVuC1IAlgTkT4Whgzq7mIeAEYk3seQLkbyF4GpuXavUQ2Epg3Prd8OlnhaGa2Srrl6V0zMzMz61zdcqSvM0XENHJH02ZmZmZF5JE+MzMzswJw0WdmZmZWAD3+9O6q2GKdLbj9oFvrHYaZWbs5f5lZJR7pMzMzMysAF31mZmZmBeCiz8zMzKwAfE1fGc8ufIX9fn921e1vPfj8GkZjZla99uYvcA4zKwqP9JmZmZkVgIs+MzMzswJw0WdmZmZWAC76zMzMzArARZ+ZmZlZATRM0SdpuKS3JE2TNFPSEVWu11/SDEkLJR1V6zjNzEry1TRJ96xiP2tL+o2k+yU9KOlHnR2rmRVHo31ly6yIGCNpAPAXSTdExPI21lkCHAKcUPvwzMw+NCsixnSwjy8Cf46I7wFIGtTxsMysqBpmpC8vIhYBc4FD09HvNEkXVWi7PCLmdm2EZmYtSRov6SpJN0maJWl/SbdJelzSdhVWewfYUdJmABHxZtdFbGY9TaON9AEgaWNgCPA9YNeImCdptQ72OQ4YB7DmkAEdD9LMim6kpGlp+hXgWeDNiDhW0lnAuIjYV9LBwPHAKWX6uBYYDFwnaT3g2xHxq9JGzl9mVo1GG+kbKele4BrgbOCNiJgHEBErOtJxREyMiFERMarPgLU6IVQzK7hZEdGUHkemeY+mny8Dj+Wmy562jYgVEXFhROwE7Ab8QNKaZdo5f5lZmxqt6JsVEXtExJ7ADcAgSUMAJDXavphZ8USFaZVrnG4IaS7y3gKW1iowM+v5GvL0LkBEhKSvADdJep/sCPpr5dpKmgJsA7wnaZeI8E0dZlZr+dO7ADNXoY9tgeslLQX6ABdExN87IzgzK56GKfoi4gVgTMm8qcBOVax7YI3CMjNbScpX67ayfFJueiZQ9uukIuJm4ObOjs/Miqlhir5qSPohMDo3a2lE7F2veMzMquX8ZWa11qOKvoj4er1jMDNbFc5fZlZrvvnBzMzMrAB61EhfZ9linY259eDz6x2GmVm7OX+ZWSUe6TMzMzMrABd9ZmZmZgXgos/MzMysAHxNXxnPLpzH/r+9sM12t3z2tC6IxsysetXmr2bOY2bF4ZE+MzMzswJw0WdmZmZWAC76zMzMzArARZ+ZmZlZAbjoMzMzMyuAuhZ9kgZKmpYeCyXNSNNPSBoiaR1Jx+Taj5d0VDu3MVzSVEkPSjq78/fCzKyllHfeyuW3ezrQV1PKYdNSHjuyM2M1s+Ko61e2RMTbQBOApGnAURHxcvNyScOBY4CrO7CZC4BvR8QDku6W9NuI+GsH+jMzq8asiBjTkQ5SDvwxsG9EvC5pdWD3TojNzAqoW57eTUe0Q4HTgJHp+f4lbQ6X9ICk6ZLObaW7HSLigTR9C7BbjcI2M6sonam4StJNkmZJ2l/SbZIel7RdhdWOAH4WEa8DRMTyiFjlUUMzK7ZuWfTlXEh2tNwUEbc0z5S0LnA6sGdE7AJ8spWkmd/HhcB65RpJGidppqSZS99+t5PCN7MCaz5gnSbp2jTvzYg4CLgRGBcR+wL/ARxfoY9hwJy2NuT8ZWbV6O5FXyWbA5sCd6XTwpul5+V8kJseCLxZrlFETIyIURExqs/AtTszVjMrpuYD1qaIaL4O79H082Xgsdz0oAp9zAE2aWtDzl9mVo3uXvQtpfx1h88Bs4ExEdEE/BNwW4U+/izp02l6X+D+zg7SzKxKUWFaFdpfB3xZ0voAklaTtGetgjOznq27/+/ducASSZOBCc0zI2KBpIuBqZJWAMvIbviYW6aPbwJXSOoD3BYRT3dB3GZmI9OZiGYz29tBRLwg6XTgekmrkeXsCW2sZmZWVrcp+tKI3UrTZKNzze7JtZkMTK6i3+eAPToeoZlZdSLiBWDdVpZPyk3PBCp+FVVETAOmdV50ZlZU3abo6yhJI1j5CHhiRFxXj3jMzNpD0g+B0blZSyNi73rFY2Y9T48p+iLiKdJ3/pmZNZqI+Hq9YzCznm2Vb+SQ1DddJ2dm1nCWLFnC0qVL6x2GmVmXqXqkT9KPgBsi4uH0Rcm/AULS/4uIKTWLsA62WGcDbvnsafUOw8w60RlnnMHnPvc5Ro8ezS233MJhhx2GJH7961/XO7RO5fxlZpW0Z6TvSOCJNH0u2YXHBwHnd3ZQZmad7dprr2XbbbcF4Lvf/S6TJk3ipptu4uyz/S+5zawY2nNN31oR8Z6k9YCPpbtnkVTpS5HNzLqN9957j7XWWosFCxbw3HPPceihhwLw4osv1jkyM7Ou0Z6i7xlJR5L9N4y7ACQNBpbUIjAzs8605ZZbcu211zJ79mz22msvAObPn0/fvn1ZvHhxnaMzM6u99hR9JwGXkH0R8nFp3j7AnZ0dVL09+9Z89p98eattbjn037ooGjPrDBMmTOCUU06hd+/eXHnllQDccccd7L333kyaNKmNtRtHNfkLnMPMiqjqoi8iHgE+XTLvWuDa8muYmXUfO+64Iw899FCLeUceeSRHHnlkjyr6zMwqadf39EnaC/g8sH5EHChpFDAgIqbWJDozs0501113cf311/P6668zZcoUZs6cyaJFi+odlplZl6j67l1JJwOXAc8Cu6XZS4Dv1yAuM7NOdemll3LiiSeyxRZbcP/99wPQt29fzjnnnDpHZmbWNdrzlS2nAmMi4gLggzTvr8BWnR6VmVknu/jii7n77rs566yz6NUrS31bb701f/vb3+ocmZlZ12hP0dcfmJOmI/3sDfgr7c2s21u8eDHDhg0DQBIAy5Yto08f/2MhMyuG9hR99wNnlcz7d+DeVd24pIGSpqXHQkkz0vQTkoZIWkfSMbn24yUd1c5tXCXp0dTvjasaq5k1tt12240LLrigxbz//u//Zo899qj5tiUNl/RWykMzJR1R5Xr9U15c2N7cZ2ZWqj03cpwMTJH0b0B/SX8DFgEHrurGI+JtoAlA0jTgqIh4uXm5pOHAMcDVq7qN5OSImN7BPsysgV166aUceOCBXH755SxevJitttqKAQMGMGXKFH71q191RQizImKMpAHAXyTdEBHL21hnCXAIcELtwzOznq49Rd88YMf02JTsVO/DEfFBq2utguYCEDgNGJme/1dJm8PJRhoF3BkR322lywslvQ/8JCJ61j/aNLOqbLDBBjzyyCM88sgjvPjiiwwbNozRo0d/eH1fV4mIRZLmAodK+ney7z59NCK+VqbtcmBu8+loM7OOqKrok7Qa8A6wTkQ8DDxc06g+ciEwIiLGpDh2TD/XBU4Hdo2IZZJ+J2m7iHi8TB9nRMR8SYOAeyQ9EhHPlTaSNA4YB7Dm4EG12h8zq4MVK1bQr18/Fi5cyOjRoxk9enTdYpG0MTAE+B5ZDpuXcmxH+nT+MrM2VXWIGxErgGeA9WobTtU2JxttvCuNAm6Wnq8kIuann2+S/fu47Su0mxgRoyJiVJ8B/WsStJnVx2qrrcaWW27JggUL6hnGSEn3AtcAZwNvRMQ8+DDHrjLnLzOrRntO714L3CzpEuBlPrqDlxp+OfNSysf4HDCb7CtklkvqRXaadyWS1omIhZL6ADsDv6xRrGbWjR155JEccMABnHLKKQwdOpQ6nDKdlTtrIWC8pCER8YakXrW4VMbMLK89Rd+J6ef4kvkBfKxTolnZXGCJpMnAhA83GLFA0sXAVEkryK6JOSa1L/VrSf3Ivl5mUkQ8WaNYzawbu+yyywAYP358i/n1uF4uIkLSV4Cb0vXGjwIrXdMHIGkKsA3wnqRdIsI3dZjZKmnP/97drJaBRERTuWlg39z0Pbk2k4HJVfS7TyeEZ2YN7vnnn6+4rNaFX0S8AIwpmTcV2KmKdVf5GxLMzPLa9b93uzNJI8iNBiYTI+K6esRjZtYekn4I5O8wWRoRe9crHjPreaou+iTNIXcdX15EbNJpEa2iiHiK9J1/Zmalhg0bVpdTudWKiK/XOwYz69naM9JX+m3w/wCcAlzfeeGYmdXGpEmTWjx/7bXXuOSSS/j85z/PqaeeWqeozMy6jiLKDt5Vt7K0IXB7ROzQeSHV36hRo2LmzJn1DsPMamzu3LmMHTuWP//5z7MiYlS94+kMzl9mxSOpqhzW0a+if5/sO/LMzBrOGmus0eoNHmZmPUl7rukr/TdnawH7Abd1akRmZjVw7rnntnj+3nvvceutt7Lvvvvy61/7vzOaWc/Xnmv6hpU8f5fs36Rd03nhmJnVxpw5c1o8X3vttTnttNM4+uijXfSZWSG0p+j7ZkSs9OXH6bq+cl+K3LBmv/UmB/zm2lbb3HzYkV0UjZl1hh/84AdsuOGGK82fO7dHpa8285dzl1lxteeavmcqzH+qMwIxM6ulLbfcsuz8ESNGdHEkZmb10Z6ib6UvuJI0APD/izSzbq/cNxUsWrSIXr06ej+bmVljaPP0bu5LmftKeqlk8XrAr2oRmJlZZ2j+UuYlS5awySYtv0d+wYIFfOELX+CKK66oU3RmZl2nmmv6jiIb5bsVODo3P4B5EfG3WgRmZtYZJk2aRESw3377cc01H913JokNNtiArbbaykWfmRVCm0VfRNwHIGlwRLxX+5DMzDrP7rvvDsD8+fNZa6216hyNmVn9VH33bkS8J2kHYFdgMLlr/CLi3IordhJJw4FHgT8D/YALI+K6KtbbB/gO2RdJvwscHRELahepmXVHa621Fo899hgPPPAA8+fPL3uNX604f5lZd1D1FcySxgEPAnsC3wC2A04HNq9NaGXNioimFMP5kqopWp8Gdo+I3YGbAf+TTbMCmjhxIjvvvDNTp07lP//zP3n88cf58Y9/zOzZs7sqBOcvM6ur9ty29nVgbEQcAixJPw8DltUkslZExCKy7wY8VNKDkqZJuqhC25ci4v30dCmwvFw7SeOk/9/e/YfZUdZ53n9/kMSoQBKgZQehicA6O0iCmhYVyU6U30PAYRZWRXDzuG5G8RqvARTRB4VFRxgGUZyFdTOz84ACooAI4YeIYAcwIHQARUFHosEEDYYfMQpKCPk8f5zqWBxOJ92nu+ucdH1e15Wrq+66665vncHvfOuuOqc0IGlg3dq14xN4RHTMOeecw7e+9S2uvvpqXvayl3H11Vdz5ZVXMmnSpErjSP6KiE4ZyY8zv9L27cXyBklb2b5R0qZ/xXgcSHoV0AN8Gphj+zFJL9nMPjsBfwcc1Gq77YXAQoBpe+xe3X2fiKjEb37zG+bMmQPAVlttxYYNGzjssMN4z3uq/bHi5K+I6JSRFH0rJc2wvZzGDzW/Q9LjNK4+qzJb0ndpfHP4E8CHbT8GYPv5oXYqfk/wSmCB7d9UEmlEdJVddtmF5cuXM2PGDF7zmtdwzTXXsOOOOzJ58uSqQkj+ioiOGsnt3XOAvyiWzwQuAW6l8ZBxVZbafpvttwNfB7aX1AMgqeW5SHoZcDXwWdvfry7UiOgmp5xyCg899BAAn/rUpzjuuON4+9vfzumnn15VCMlfEdFRI/n27kWl5RslTQcm2/79eAQ2jHgs6UPAtZKepfHNuBNbdP0QsA/wMUkfA262/Q8VhhoRXWD+/Pkblw877DCeeuop1q1bxzbbbMMJJ5xQaSzJXxHRCSO5vYukHYC/Av7M9jmSdpQ0zfbK8QnvT4rbygc2td0KvGUz+50LnDt+kUXEluKJJ57ghhtu4Ne//jWnnHIKjz/+OGvWrBn34yZ/RUQ3GMlPtvwl8FPgPcAni+b/CPzvcYirLZLOKb4JN/jv252OKSK6w+LFi/nzP/9zLr30Uj796U8D8LOf/YwPfvCDHY6sIfkrIsbbSGb6vgC80/Ytkp4q2r4P7Dv2YbXH9imdjiEiutPf//3f87WvfY0DDjiA6dOnA/CmN72Ju+++u8ORNSR/RcR4G8kXOWbYvqVYHvxJgHWM8BZxREQnLF++nAMOOABovHcXYPLkyaxf3/Kn7yIiJpyRFGwPSjrE9k2ltgOBB8Y4po7bc/r2XHd0tb/dFRHja6+99uKmm27ikEMO2dj2ne98h5kzZ7J48eIORja2kr8iYigjKfpOBq6TdD3wMkn/BzgCeMe4RBYRMYY+97nPMW/ePA4//HD+8Ic/8Ld/+7csWrSIa665hn337ZqnVCIixs1mb+9K+g8Atu8CZgE/Bv4N+AWwr+17xjXCiIhRWLVqFQBvfvOb+eEPf8hrX/ta3ve+9/HqV7+au+++mze+8Y0djjAiohrDmen7d2A7ANu/kvRm238zvmFFRIyN17zmNawt3ke78847c9ddd/GNb3yjw1FFRFRvOEWfmtbnjkMcXeXhp9ZwxJVXv6Bt0dFHdSiaiBgN+4Wvou3v7+9MIBERHTacb+/m5d0RscUa/KZuRETdDWemb2tJb+NPM37N64O/LB8R0XXWr1/Pd7/73Y0zfs3rERF1MZyi7zc0vrgx6ImmdQO7j2VQERFj5ZWvfCXve9/7Nq7vsMMOL1jPTGBE1MVmiz7bMyqIIyJiXCxfvnyzfVL4RUQdjOSNHBERERGxheraok/SDElPlV4+fsvm92o5ziskXSnpNknfk3TuWMcaERObpFmSbixy0RJJJw3Rb76k01q0nypp5giP+QpJX5S0uPj3LUlvaPccIiK6/b25S20fOMox/hvwA9ufBpC0/ejDioi6kLQdcAlwlO1latwLPngkY9g+u41Df55G7vpwEUcPsHMb40REAF0809eKpDMkXSTpWklLJR1eXH0/sImr6N8Db5T0agDbTw4x9gJJA5IG1sAXjrwAACAASURBVBU/5BoRAcwDFtleBuCGmyRdXMz83SvpyFL/fSQtknSfpDkARd7av1h+RNL5ku4a6s6DpK2At9m+YLDN9mrbPxii/8b8tXr16jE67YiYaLq96Jtdur17adH2pO0jgSuABbYPAz4JvH+IMS4F+oHLJP27pHe36mR7oe0+232Tt9tujE8jIrZguwIrWrSfYHsucBDw2VL7traPAI6iMVvXbCfgLOAtwLxiJrFZD/D44IqkzxVFYssZw3L+6unpGc45RUQNbVG3dyWdAdxXrK4E7i8tt7xta/t54DzgvOI9wndJutr2H8ct6oiYSFYAe5cbipm40yXtB6wHdittvgfA9nJJU1uM96jtVcU4K4HpQPPthdU0Cj+KsU6WNBc4bnSnEhF11u0zfa14iOWWv7lQfCFkSrH6FLBuvAKLiAnpOhozcnuU2k4GZgFzgKOBDaVtswEk9fLiYg5e/JajF+Uu2xuAWyV9qNTc7RfpEdHluj2JzJbUX1ofaGOMvYHLJa0DJgNnZ5YvIobL9lpJxwMXFBeQk2kUgpNoPDpyP7CmtMszkq6n8aWLE0dx6BOBsyXdBvwReI7GbeGIiLYoryJ6sWl77Ok5//hPL2hbdPRRHYomIsabpKW2+zodx1jo6+vzwEA718cRsaUabg7r9pm+EZF0DrBvqWmd7RH9tEJERJUk7QVc2NS80PZlnYgnIiauCVX02T6l0zFERIyE7QeBuZ2OIyImvi3xixwRERERMUITaqZvrOw5fVqe4YuIiIgJJTN9ERERETWQoi8iIiKiBlL0RURERNRAir4WHn5qLX995c389ZU3dzqUiIiIiDGRoi8iIiKiBlL0RURERNRAir6IiIiIGkjRFxEREVEDXVn0SZoh6SlJ/ZIGJB07irHOkHTcWMYXERERsaXpyqKvsNT2XODtwGclbfLtIZJeUklUEREFSVOLi9N+SWsk3Vks/0hSj6Rpkt5b6j/ii1BJX5e0RNL3Jc0f85OIiNro+tew2V4raRXwL5JmAFOBL9leKGku8HFgLbBM0oXAQuBlwHrgoGKYt0n6r0Av8C7bP6n2LCJiIrL9W2AugKR+4DjbKwe3FznrvcCXR3GY/9f2zyRNAX4k6XLbfxzFeBFRU11f9El6FdADHGD7aUkvBR6Q9P8VXXYG5tl+TtLXgfNsf1vSVrY3SAL4ne3/Xtwmfj/wkRbHWQAsAHjZjq+s4MwiYqIaLACBk4DZxfo/NfU5BvgwIODbts9sNZbtnxWLzwEbALc43sb81dvbOybnEBETTzff3p0t6bvAV2gksw9KugP4NvDK4h/AgO3niuXXAt8FsL2hNNbS4u8vgR1aHcz2Qtt9tvsmbzd1bM8kIurqPIpHVWxfP9goaTpwMvB22/sDr5c0czNjfQL4qu1nmzeU81dPT89Yxh8RE0g3z/QttX0gbEyQ/wzMBCYBP6VxdQzwfGmfH9O41XLz4Exf0V6+MhYREZ21J7AbjVwFMK1Yf6BV5+K5wL2Bd1cVYERMPN1c9JWtoVHQ3QE8BDwxRL+P0Hj27zTgOUkHVxRfREQr62idZ38OPAwcaHu9pK0Y4oJU0juAY4Ejm+5gRESMSFcWfbaXAweW1g0c06LrSqC/1O+XwCFNfc4obb+DRuEYEVGFVcAfJF0FXDjYaPsJSV8AbpX0PI3n9d5b9G92KfAT4NvFrOB7bD867pFHxITTlUVfRMSWpviJqRctA4eVlm8p9bkKuGoY424zBuFFRKToi4joBpL2ojQbWFho+7JOxBMRE0+KvoiILmD7QYrf/IuIGA/d/JMtERERETFGMtPXwp7Tt+ObRx+0+Y4RERERW4jM9EVERETUQIq+iIiIiBpI0RcRERFRAyn6Wlj21NP8l6vu7nQYEREREWMmRV9EREREDaToi4iIiKiBFH0RERERNZCiLyIiIqIGUvRFRERE1EBXFn2SZkh6SlJ/8e+WUYx1qKTbJS2WdKOkPccy1oioL0lTS3lqjaQ7i+UfSeqRNE3Se0v9z5B03AiPMUPSrZK+J+kTY38WEVEX3fwatqW2DxzNAJJ2B84GDrD9hKTXA1dJmm17/ZhEGRG1Zfu3wFwASf3AcbZXDm6XNAN4L/DlURzmbOB027dL+o6kb9j+ySjGi4ia6sqZvlaKK+SLJF0raamkw4uZuwckzRxit3cDF9p+AsD2fcCDwJtajL9A0oCkgWfXrhm/E4mICa+Y7dsFOAmYXawf3tTnmOIuxB2SPrWJ4V5n+/Zi+XrgP7c43sb8tXr16jE7j4iYWLq56BtMlP2SLi3anrR9JHAFsMD2YcAngfcPMcYuwC+b2h4Bdm3uaHuh7T7bfS/dbtoYnUJE1Nx5NO5azLV9/WCjpOnAycDbbe8PvH4TF6/lPL0G2KG5Qzl/9fT0jGH4ETGRbDG3dyWdAdxXrK4E7i8tbz/EGI8CvU1tvcANYxdmRMSI7QnsBtwsCWBasf5Ai74bSstTgSfHPbqImJC6eaavFQ+xrCH6Xw58UNL2AJL2AfYGloxPeBERL7CO1hfXPwceBg60PRd4A3DjEGP8QNJ+xfJhwG1jHWRE1EM3z/TNLh6MHjQw0gFsPyzpNOAaSS+lcTV9WL7EEREVWQX8QdJVwIWDjcUXy74A3CrpeeA5Gl/4WNVijI8D/1fSZOBG2w9VEHdETEBdWfTZXg5M38T2S0rLA8CQP4FQPEdzvaQpwDdpfIlj2ZgFGxEBFDN2L1qmMTs36JZSn6uAq4Yx7s+Bt40+woiou64s+toh6Rxg31LTOtsHD67Y/iNwaOWBRUQMg6S9KM0GFhbavqwT8UTExDNhij7bp3Q6hoiIdtl+kOI3/yIixsOW9kWOiIiIiGhDir4W9pj+Cq76L/tuvmNERETEFiJFX0REREQNpOiLiIiIqIEUfRERERE1kKKvhZ+veZZ3feMXnQ4jIiIiYsyk6IuIiIiogRR9ERERETWQoi8iIiKiBlL0RURERNRApUWfpFmSbpTUL2mJpJOG6Ddf0mkt2k+VNHOEx+yXdGfxt1/SQe3GHxFRJmlqKbesKeWaH0nqkTRN0ntL/c+QdNwIj3GRpPuKca8Y+7OIiLqo7N27krYDLgGOsr1MkoCDRzKG7bPbPPwxtle2uW9EREu2f0vxvlxJ/cBx5VwjaQbwXuDLozzU39m+Y5RjRETNVTnTNw9YZHsZgBtuknRxcQV7r6QjS/33kbSouMKdAxuvePcvlh+RdL6kuySdW+F5RERsUpHTdgFOAmYX64c39TlG0u2S7pD0qc0MeV7R953jFnRETHiVzfQBuwIrWrSfYPtpSTsAi4Fri/ZtbR9aXClfCfQ17bcTcBbwGPCQpDNtrx3i2FdIerZY/jvbDzR3kLQAWADw8h13Hv5ZRUQM7TxgL9sHAkh6Y/F3OnAyMMf2c5KuljSzVW4CPmL7cUnbA7dIusf2z8sdyvmrt7d3PM8nIrZgVRZ9K4C9yw2StgJOl7QfsB7YrbT5HgDbyyVNbTHeo7ZXFeOsBKYDQxV9m729a3shsBBg+z1nevOnExHRtj1p5LubG0+6MK1Yf1HRZ/vx4u+Tkm4G9gF+3tRnY/7q6+tL/oqIlqq8vXsdME/SHqW2k4FZwBzgaGBDadtsAEm9tC7mmhObxi7UiIgxsY7WF9c/Bx4GDrQ9F3gDcGOrASRNK/5OBt4K/Pu4RBoRE15lM32210o6HrhA0hRgMo1CcBLQD9wPrCnt8oyk64GdgRNHefjy7d3/ZfvKUY4XETEcq4A/SLoKuHCw0fYTkr4A3CrpeeA5Gl/4WNVijK9J2oZGrrzE9o8riDsiJiDZuRPQbPs9Z/rgc67l8r95dadDiYgKSFpqu/m54S1SX1+fBwYGOh1GRFRouDmsymf6xpWkvShdSRcW2r6sE/FERIxEclhEjLcJU/TZfpDi97IiIrY0yWERMd7yGraIiIiIGkjR18Lu016a5/kiIiJiQknRFxEREVEDKfoiIiIiaiBFX0REREQNpOiLiIiIqIEUfS08tuY5zru61Q/jR0RERGyZUvRFRERE1ECKvoiIiIgaSNEXERERUQMp+iIiIiJqoCuLPkkzJD0lqb/4d8soxpor6dZinO9Jes9YxhoR9SFplqQbi3yyRNJJQ/SbL+m0Fu2nSpo5guOdVRzrJ5J+USz/62jOISLqa+tOB7AJS20fOJoBJM0APgccZvs3krYG/nIMYouImpG0HXAJcJTtZZIEHDySMWyfPcL+Hy+OPR/YxfZnRrJ/RERZV870tSLpDEkXSbpW0lJJhxdX3A9s4sr5WOBLtn8DYHu97bZnDSOi1uYBi2wvA3DDTZIuLmbg7pV0ZKn/PpIWSbpP0hyAIoftXyw/Iul8SXdJOrf604mIuunmom926fbupUXbk7aPBK4AFtg+DPgk8P4hxtgVWDGcg0laIGlA0sDTa58YdfARMeEMlU9OsD0XOAj4bKl9W9tHAEcBn2+x307AWcBbgHnFTGJbyvlr9erV7Q4TERNcNxd9S23PLf4NPod3X/F3JXB/aXn7IcZYAfQO52C2F9rus933iu12aDvoiJiwXpRPJG0FnC7pDuAqYLfS5nsAbC8HprYY71Hbq2ybRh6b3m5g5fzV09PT7jARMcF1c9HXiodY1hD9LwP+VtIrASS9RNLbxyu4iJjQrqMxI7dHqe1kYBYwBzga2FDaNhtAUi+wtsV4blofKo9FRIyJbv4ix2xJ/aX1gZEOYHu5pJOByyW9hMb5XjhG8UVEjdheK+l44AJJU4DJNArBSUA/jbsPa0q7PCPpemBn4MSKw42IeBE17ixE2a577uMT/+kmTjrqP3Q6lIiogKSltvs6HcdY6Ovr88DAiK+RI2ILNtwc1s0zfSMi6Rxg31LTOtsj+jmFiIhOkbQXL74TsdD2ZZ2IJyImnglT9Nk+pdMxRES0y/aDwNxOxxERE9eW9kWOiIiIiGhDir4Wdpo2Kc/zRURExISSoi8iIiKiBlL0RURERNRAir6IiIiIGkjRFxEREVEDKfpaeOqp9Z0OISIiImJMpeiLiIiIqIEUfRERERE1kKIvIiIiogZS9EVERETUQGVFn6RZkm6U1C9piaSThug3X9JpLdpPlTRzhMd8uaQvFse8XdLXJe3Q7jlERL11KI/1S7qz+HtLu7FHRGxdxUEkbQdcAhxle5kkAQePZAzbZ7dx6POAH9n+cBHHTOClbYwTETXXwTwGcIztlW3uGxEBVDfTNw9YZHsZgBtuknRxcfV6r6QjS/33kbRI0n2S5gBIukjS/sXyI5LOl3SXpHNbHVDSVsCBwAWDbbYfsP2r8TrJiJjQKs9jERFjqZKZPmBXYEWL9hNsP13ccl0MXFu0b2v7UEkzgCuBvqb9dgLOAh4DHpJ0pu21TX16gMdtezgBSloALADYccddhrNLRNRLJ/LYoCskPQvcafvjzRvL+au3t3dkZxURtVFV0bcC2LvcUMzEnS5pP2A9sFtp8z0AtpdLmtpivEdtryrGWQlMB5qT5WpgR0kaTuFneyGwEGCPPV43rEIxImqlE3ls0CZv75bzV19fX/JXRLRU1e3d64B5kvYotZ0MzALmAEcDG0rbZgNI6qV1EmxOanpRB3sD8B3gQxs7Sa+VtHM7JxARtVd5HouIGEuVzPTZXivpeOACSVOAyTQS6CSgH7gfWFPa5RlJ1wM7AyeO4tAnAf8oaTGNAvfXwAdGMV5E1FQH81hExJjQMB95q5U99nidly27v9NhRERFJC213fzM3Rapr6/PAwMDnQ4jIio03BxW1TN940rSXsCFTc0LbV/WiXgiIkYqeSwixtuEKPpsPwjM7XQcERHtSh6LiPGW17BFRERE1ECKvhamT58QE6ARERERG6Xoi4iIiKiBFH0RERERNZCiLyIiIqIGUvRFRERE1ECKvoiIiIgaSNEXERERUQMp+iIiIiJqIEVfRERERA2k6IuIiIiogUqLPkmzJN0oqV/SEkknDdFvvqTTWrSfKmnmCI/ZL+nO4u9tkma0F31E1Fkn8lex336SNkjavZ24IyIGVfa+MUnbAZcAR9leJknAwSMZw/bZbR7+GNsrJR0PfAj4aJvjREQNdTh/vQc4HzgW+EybY0REVDrTNw9YZHsZgBtuknRxceV8r6QjS/33kbRI0n2S5gBIukjS/sXyI5LOl3SXpHOHGcNU4LdjeVIRUQsdyV+SJgGvB04FDhu3s4uIWqhspg/YFVjRov0E209L2gFYDFxbtG9r+9DiduyVQF/TfjsBZwGPAQ9JOtP22iGOfUXjwpxe4K2tOkhaACwA6O3tHe45RUQ9dCp/HQLcYPtZSfdLmm17aXOn5K+IGI4qZ/pW0Ci6NpK0FXC6pDuAq4DdSpvvAbC9nMYMXbNHba+ybWAlMH0Txz7G9luAI4Avtepge6HtPtt9PT09wzyliKiJTuWvY4GDJH2Lxozfca06JX9FxHBUWfRdB8yTtEep7WRgFjAHOBrYUNo2G0BSL9DqCthN6xpGDE8CyYgRMVKV5y9J2wC72P5L24fa3g94q6SXtH8aEVFnld3etb22+CLFBZKmAJNpJNJJQD9wP7CmtMszkq4HdgZOHOXhr5D0LDAF+Ngox4qImulQ/vob4Lamth8CBwDfbnPMiKgxNe4uRFlfX58HBgY6HUZEVETSUtvNz91tkZK/IupnuDmsyi9yjCtJewEXNjUvtH1ZJ+KJiBiu5K+IqMKEKfpsPwjM7XQcEREjlfwVEVXIa9giIiIiaiBFX0REREQNpOiLiIiIqIEUfRERERE1kKIvIiIiogZS9EVERETUQIq+iIiIiBpI0RcRERFRAyn6IiIiImogRV9EREREDaToi4iIiKiByoo+SbMk3SipX9ISSScN0W++pNNatJ8qaeYIj9kv6U5Jd0j6nqT5bYYfETXX4RzWL+k2STPaiz4iArau4iCStgMuAY6yvUySgINHMobts9s8/DG2V0qaBnxT0iO2v9vmWBFRQ12Sw44HPgR8tM1xIqLmqprpmwcssr0MwA03Sbq4uIK9V9KRpf77SFok6T5JcwAkXSRp/2L5EUnnS7pL0rnDCcD2GuAfgGPH9tQiogY6nsOAqcBvx/KkIqJeKpnpA3YFVrRoP8H205J2ABYD1xbt29o+tLiVcSXQ17TfTsBZwGPAQ5LOtL12GHGsAF7VaoOkBcACgN7e3mEMFRE10skcdkVjYpFe4K2tOiR/RcRwVDXTt4JGwtpI0lbA6ZLuAK4CdittvgfA9nIaV7fNHrW9yraBlcD0YcaxK/Boqw22F9rus93X09MzzOEioiY6mcOOsf0W4AjgS606JH9FxHBUVfRdB8yTtEep7WRgFjAHOBrYUNo2G0BSL9Dq6tdN69pcAJKmAh8HLh9+2BERQBfkMOBJIBVdRLStktu7ttcWDyFfIGkKMJlGEp0E9AP3A2tKuzwj6XpgZ+DEUR7+CknP0yhw/832LaMcLyJqpgty2LPAFOBjoxwrImpMjbsLUdbX1+eBgYFOhxERFZG01Hbzc3dbpOSviPoZbg6r6osc40rSXsCFTc0LbV/WiXgiIkYiOSwiqjAhij7bDwJzOx1HREQ7ksMiogp5DVtEREREDaToi4iIiKiBFH0RERERNZCiLyIiIqIGUvRFRERE1ECKvoiIiIgaSNEXERERUQMp+iIiIiJqIEVfRERERA2k6IuIiIiogRR9ERERETVQadEnaZakGyX1S1oi6aQh+s2XdFqL9lMlzWzjuPtJ2iBp93bijojoRP6S9HJJXyyOebukr0vaod1ziIh627qqA0naDrgEOMr2MkkCDh7JGLbPbvPw7wHOB44FPtPmGBFRUx3MX+cBP7L94SKOmcBL2xgnIqLSmb55wCLbywDccJOki4ur2HslHVnqv4+kRZLukzQHQNJFkvYvlh+RdL6kuySdO9RBJU0CXg+cChw2bmcXERNZ5flL0lbAgcAFg222H7D9q/E6yYiY2Cqb6QN2BVa0aD/B9tPFLYvFwLVF+7a2D5U0A7gS6GvabyfgLOAx4CFJZ9pe22L8Q4AbbD8r6X5Js20vbe4kaQGwAKC3t3fkZxcRE1kn8lcP8Lhtby645K+IGI4qZ/pWAC/IRsWV7OmS7gCuAnYrbb4HwPZyYGqL8R61vapIiCuB6UMc91jgIEnfojHjd1yrTrYX2u6z3dfT0zP8s4qIOuhE/loN7FjcSt6k5K+IGI4qi77rgHmS9ii1nQzMAuYARwMbSttmA0jqBVrN4DVf/b4oMUraBtjF9l/aPtT2fsBbJb2k/dOIiBqqPH/Z3gB8B/jQxk7SayXt3M4JRERUdnvX9lpJxwMXSJoCTKaRSCcB/cD9wJrSLs9Iuh7YGTixzcP+DXBbU9sPgQOAb7c5ZkTUTIfyF8BJwD9KWkzjIv3XwAdGMV5E1JiG8bhI7fT19XlgYKDTYURERSQttd383N0WKfkron6Gm8Oq/CLHuJK0F3BhU/NC25d1Ip6IiOFK/oqIKkyYos/2g8DcTscRETFSyV8RUYW8hi0iIiKiBlL0RURERNRAir6IiIiIGkjRFxEREVEDKfoiIiIiaiBFX0REREQNpOiLiIiIqIEUfRERERE1kKIvIiIiogZS9EVERETUQIq+iIiIiBqotOiTNEvSjZL6JS2RdNIQ/eZLOq1F+6mSZo7geGcVx/qJpF8Uy/86mnOIiPqpOncV+/RLulPSHZK+J2l+m+FHRACwdVUHkrQdcAlwlO1lkgQcPJIxbJ89wv4fL449H9jF9mdGsn9ERCdyV8kxtldKmgZ8U9Ijtr/b5lgRUXNVzvTNAxbZXgbghpskXVxc0d4r6chS/30kLZJ0n6Q5AJIukrR/sfyIpPMl3SXp3ArPIyLqpeO5y/Ya4B+AY8f21CKiTiqb6QN2BVa0aD/B9tOSdgAWA9cW7dvaPlTSDOBKoK9pv52As4DHgIcknWl7bbvBSVoALADo7e1td5iImHi6JXetAF7VakPyV0QMR5UzfSuAF2QjSVsBp0u6A7gK2K20+R4A28uBqS3Ge9T2KtsGVgLTRxOc7YW2+2z39fT0jGaoiJhYuiV37Qo82mpD8ldEDEeVRd91wDxJe5TaTgZmAXOAo4ENpW2zAST1Aq2ugt20rrELNSJio47nLklTgY8Dlw8/7IiIF6rs9q7ttZKOBy6QNAWYTCOZTgL6gfuBNaVdnpF0PbAzcGJVcUZElHU4d10h6XkaF+j/ZvuWUY4XETWmxh2GKOvr6/PAwECnw4iIikhaarv52bstUvJXRP0MN4dV+UWOcSVpL+DCpuaFti/rRDwREcOR3BURVZkwRZ/tB4G5nY4jImIkkrsioip5DVtEREREDaToi4iIiKiBFH0RERERNZCiLyIiIqIGUvRFRERE1ECKvoiIiIgaSNEXERERUQMp+iIiIiJqIEVfRERERA2k6IuIiIiogRR9ERERETVQedEnaZakGyX1S1oi6aQh+s2XdFqL9lMlzRzhMfsl3Vn8vaXd2COivrogd/VLOqjd+CMitq7yYJK2Ay4BjrK9TJKAg0cyhu2z2zz8MbZXtrlvRNRYcldETARVz/TNAxbZXgbghpskXVxcxd4r6chS/30kLZJ0n6Q5AJIukrR/sfyIpPMl3SXp3NEEJmmBpAFJA6tXrx7NUBEx8XRt7irGS/6KiM2quujbFVjRov0E23OBg4DPltq3tX0EcBTw+Rb77QScBbwFmFdcjQ/liiI5n9Vqo+2Ftvts9/X09AzjVCKiRrohd/UPdXs4+SsihqPS27s0kube5QZJWwGnS9oPWA/sVtp8D4Dt5ZKmthjvUdurinFWAtOBtUMcO7dIIqJdyV0RscWreqbvOhpXtXuU2k4GZgFzgKOBDaVtswEk9dI6IbppXWMXakTERsldEbHFq3Smz/ZaSccDF0iaAkymkUwnAf3A/cCa0i7PSLoe2Bk4scpYIyIGdTh3XSHp2WL5f9m+cpTjRURNyW6+4Iy+vj4PDAx0OoyIqIikpbb7Oh3HWEj+iqif4eawqp/pG1eS9gIubGpeaPuyTsQTETEcyV0RUYUJVfTZfhCY2+k4IiJGIrkrIqqQ17BFRERE1ECKvoiIiIgaSNEXERERUQMp+iIiIiJqIEVfRERERA2k6IuIiIiogRR9ERERETWQoi8iIiKiBlL0RURERNRAir6IiIiIGkjRFxEREVEDlRV9kmZJulFSv6Qlkk4aot98Sae1aD9V0swRHvMVkr4oaXHx71uS3tDuOUREbI6kGZKeKnJdv6RbRjHWoZJuL/LXjZL2HMtYI6Jetq7iIJK2Ay4BjrK9TJKAg0cyhu2z2zj054Ef2P5wEUcPsHMb40REjMRS2weOZgBJuwNnAwfYfkLS64GrJM22vX5MooyIWqlqpm8esMj2MgA33CTp4uJK+F5JR5b67yNpkaT7JM0BkHSRpP2L5UcknS/pLknntjqgpK2At9m+YLDN9mrbPxii/wJJA5IGVq9ePUanHREBks4octi1kpZKOryYuXtgE3cw3g1caPsJANv3AQ8Cb2oxfvJXRGxWVUXfrsCKFu0n2J4LHAR8ttS+re0jgKNozNY12wk4C3gLMK+YSWzWAzw+uCLpc0WR2HLG0PZC2322+3p6eoZzThERQ5ldur17adH2pO0jgSuABbYPAz4JvH+IMXYBftnU9giNfPoCyV8RMRyV3N6lUfDtXW4oZuJOl7QfsB7YrbT5HgDbyyVNbTHeo7ZXFeOsBKYDa5v6rKZR+FGMdbKkucBxozuViIjNesHtXUlnAPcVqyuB+0vL2w8xxqNAb1NbL3DD2IUZEXVS1UzfdTRm5PYotZ0MzALmAEcDG0rbZgNI6uXFxRyAm9b1og72BuBWSR8qNVdV5EZENPMQyy/KX4XLgQ9K2h5A0j40Lp6XjE94ETHRVVIE2V4r6XjgAklTgMk0CsFJQD+N/FswoQAACWxJREFUq941pV2ekXQ9jS9dnDiKQ58InC3pNuCPwHM0bgtHRIyn2ZL6S+sDIx3A9sPFLxlcI+mlwDTgsHyJIyLaJbt50iz6+vo8MDDiHB0RWyhJS233dTqOoRQXy98Evmz7sk31Tf6KqJ/h5rAJcbtT0l7AhU3NCzeXHCMiuoGkc4B9S03rbG/8WSvbfwQOrTywiJhQJkTRZ/tBYG6n44iIaIftUzodQ0RMfHkNW0REREQNpOiLiIiIqIEUfRERERE1kG/vtiDpd8BPOx3HJuxI6W0jXSjxta+bY4OJG99utifEqyySv0Yt8Y1O4hudcc1hE+KLHOPgp13+8w0Dia993RxfN8cGiW8Lkfw1ColvdBLf6Ix3fLm9GxEREVEDKfoiIiIiaiBFX2sLOx3AZiS+0enm+Lo5Nkh8W4Ju/wwS3+gkvtGpdXz5IkdEREREDWSmLyIiIqIGalf0SZovaYmk70l6Q9O2KZIulXR78XdK0T5D0q3FPp/owvguknSfpH5JV3Qwvv0kPSDpj5J2KbV3y+c3VHzd8vmdIun7xbZ/lqSi/Q1F2xJJ87swvn5JdxZ//7mD8b1L0h2SbpN0naTtivbK/vurQjfnsOSvjsTXLZ9f8tfo4qsmf9muzT9gOnAvMBl4NXBH0/YPAJ8slj8FfKBYvhyYUyx/B/hPXRbfRcD+XfD5TQW2AfqBXUrt3fL5DRVft3x+/7G0/HXggGL5e0X/ycX+07ssvhd8nh38/CaXls8EPlTlf39V/OvmHJb81bH4uuXzS/4aXXyV5K+6zfS9Cbjd9jrbvwC2kfTS0va5wHXF8iLgPxfLr7N9e7F8fam9W+IDOK+4gn7nOMW22fhs/9b271vs1xWf3ybig+74/H5W6rsOWF9sf4XtX9heB9wOvLFb4hvcBFxeXI2+fZxiG05860p9Xw78uFiu6r+/KnRzDkv+6kx80B2fX/LX6OKrJH/VrejbHniqtP7boq3V9jXADsVy+XMqt3dLfB+xvS/wDuBUSbt3KL6hdMvnN5Su+vwkzQX+DLiNxme1prS5459fU3wAx9jeH/hvwP+WtG2n4pP03yU9QCMxDibNqv77q0I357Dkr9FJ/qo+PqhZ/qpb0fckMK20PrVoa7W9vG3DJvbpeHy2Hy/+PgncDOzTofiG0i2fX0vd9PlJmgWcBbzTjfn8J4t+Q+7T4fjKn98K4AfAnp2Kz/b/tT0TuBL4aNFc1X9/VejmHJb8NTrJX9XHV7v8Vbei7/vA/pImSeoFfm/72dL2xcBfFct/VawD/EDSfsXyYfzpCqEr4pM0rfg7GXgr8O8dim8o3fL5tdQtn5+kPYF/A95VSkR/BJ6R1CtpErA/cHe3xKeGwQeOtwVmAo90KL4ppb5rgGeK5ar++6tCN+ew5K8OxNctn1/y16jjqyZ/jeaBwC3xH/A+YAmNh0v7gNcBHy22vQz4Ko3nDr4KTCnadwe+W+xzWhfGd1PR/27gwx2M7zU0HjR9qojxg132+Q0VX7d8ftcBD9N4sLgfOLxo7yv6LwHe103xAZOApcAdNJLaOzsY3ydLsX0DmFb1f39V/GszR1TyGbQZW7f87y/5a3TxJX+NLr5K8ld+nDkiIiKiBup2ezciIiKillL0RURERNRAir6IiIiIGkjRFxEREVEDKfoiIiIiaiBFX3QNSXMlrRzF/l+S9MmxjKkY98fFr7hHRLSU/BVbghR9MaYkLZf0B0m/l7RK0kWSthmH48yXdEe5zfYHbH96rI9l+7W2+8d63HYUn++BnY4jYiJK/hpfyV+dl6IvxsMRtreh8cOTrwc+3uF4tniStu50DBE1kfw1xpK/ukeKvhg3tlfR+LX41w22SXqppHMl/VLSY8UtjZe12l/SqZKWSfqdpAclHVW0/wXwJeAtxRX5mqL9IkmfKZYfkjSvNNbWkh6X9IZi/c2SlkhaI+kHm7r9Ub46lXSGpCskXVLE9YCk10j6uKTfSFoh6eDSvv2SzpJ0t6TfSrpG0val7UcWt1/WFH3/oum4H5P0Q+BpSV8FeoFFxXmfMvz/a0TESCR/JX9NRCn6YtxI2oXGuwIfLjX/I43XCb2OxoutXwV8aoghlgFzaLxk+n8Cl0j6M9sPAR8A7rS9je1pLfb9KvDu0vohwOO275X0KuB64DPA9sBHgKsk9Qzz1I4AvgJMB+6j8f8YtirO5Uzg/zT1fy+N1+/sDKwHvggg6TVFnH8P9AA30EiIk0v7vpvG64Km2X438EuKmQjb5wwz3ogYoeSvjZK/JpAUfTEevinpd8AK4DfA6dB4uTXwP4ATbT9p+3fAZ4F3tRrE9hW2f2V7g+2vAT8D9h1mDJcBR0p6ebF+bNEGcBxwg+0birFvBgb408vgN+d22zfZXg9cQSPhnW37OeByYIaKl6AXvmL7R7afpvF+xf8q6SXAO4Hrbd9c7HsujfeT7lfa94u2V9j+wzBji4jRSf5K/pqwUvTFePhr29sCc4H/BOxYtPcALweWFrcD1gDfKtpfRNJ7Jd1f6rt3aaxNsv0w8BBwRJE4j+RPSXM34JjBcYux9wf+bJjn91hp+Q80rsCfL60DlB/+XlFafoTGS753pHHl/Egp5g1F31cNsW9EjL/kr+SvCSsPV8a4sb1Y0kU0rgD/GnicRlJ5re1HN7WvpN2AfwEOoHEb5HlJ9wMaHH4YIQzeItkKeLBIpNBIRF+x/T9GeErt2rW03As8R+Oz+BUwc3BDMZOwK1D+bJrPczjnHRGjlPy1UfLXBJKZvhhvXwAOkvS64krwX4DPS3olgKRXSTqkxX6voJEgVhf9/h8aV8qDHgN2aXp+pNnlwMHAB/nTVTLAJTSuoA+R9BJJU9T4ja1d2jzHzTlO0l7FFfuZwJXFlfXXgcMlHSBpEnAy8CywZBNjPQbsPk5xRsQLJX8lf00oKfpiXNleDXyZxrMgAB+j8WD0XZLWAt8B/rzFfg8CnwPupJEoZgLfK3W5FfgxsErS40Mc+9fF/vsBXyu1rwDeAXyCRlJeAXyU8fvfw1eAi4BVwBTgw0UcP6XxfM4/07hyPoLGQ87rNjHWWcBpxW2dj4xTvBFB8lch+WsCkZ3Z1ojxIqkfuMT2v3Y6loiIkUj+mngy0xcRERFRAyn6IiIiImogt3cjIiIiaiAzfRERERE1kKIvIiIiogZS9EVERETUQIq+iIiIiBpI0RcRERFRAyn6IiIiImrg/weO5GMKPX3QawAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nrows =1; ncols = 2 \n",
    "fig, axes = plt.subplots(nrows=nrows, ncols=ncols, sharex='all', figsize=(10, 10))\n",
    "\n",
    "name_classifiers = [('RandomForest', RFC_best), ('GradientBoosting', GBC_best)]\n",
    "nclassifier = 0 \n",
    "for row in range(nrows):\n",
    "    for col in range(ncols):\n",
    "        name = name_classifiers[nclassifier][0]\n",
    "        classifier = name_classifiers[nclassifier][1]\n",
    "        indices = np.argsort(classifier.feature_importances_)[::-1][:40]\n",
    "        g = sns.barplot(y=X_train.columns[indices][:40], \n",
    "                        x=classifier.feature_importances_[indices][:40], \n",
    "                       orient='h', ax=axes[col])\n",
    "        g.set_xlabel('Relative import ', fontsize=12)\n",
    "        g.set_ylabel('Features', fontsize=12)\n",
    "        g.tick_params(labelsize=9)\n",
    "        g.set_title(name + 'feature importance')\n",
    "        nclassifier += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GTaV9bho7vvTxdT+/ks6dOwHQ6/RunNGrO11OuwCAhQtep/c5vXh74ZKkvoaaVJzGsyzSd2xfRc06t2XPx5/y5badFB8Ks2HaUtpd2PWwPu6QU68OALn167L/sy8AaHPOKezcuI2dG7YC8PWe/XhxGn8Np1C3zqdw7DH1Ux1GWuvRvQsfffQxW7Zs5dChQ0yZ8gYDB1xUYf8RwwczefLrALg7ubVzycnJITc3h1rZtfj0s53JCj0p3C3hJWgqHSGbWXfgOHd/s0z7QGC7u6+syeCqU72mDdlXuLt0fV/hbvK6tD2sz+JH/saw539F11F9yK6by+RL7wGgYZum4M7QSbdQt/ExbJi6hHf+OCOp8YuUyGvelG0F3/5lV7C9kB7du5Tbt1Wr5rRu3ZK35i8GYOmylby94H8p2PouZsYTTz7Lxo35SYk7WdK5ZBFvhHw/sKGc9vXRbeUys9FmtsLMVizbv6mibkllHPltWPYfrsPAXrz/ykKePP3feWXU/fR/5FowI1Qri+bdT2T6L57gL0Mm0L5vN1qdeXKSIhc5nFl5n+Xys9DwYYN49W8zKC6OXLW9bdvWnHRSe45v041Wrbvyk3PP5OyzetZovMlW7JbwEjTxEnJjd/+4bKO75wONK9rJ3Se6ezd379azXvvvGWL12PfJbuo3a1S6Xr9ZI/Z/+sVhfU4d3puN05cBsOPdfGrlZlO3UX32Fe5m29KNHPxiP0Vff8Pm+atp2ql1MsMXKbW9oJCWLfJK11s0b0Zh4afl9h02bBCTJ79Ruj54UF+WvfMuX311gK++OsB/z3qLnj1Pq/GYkylcHEp4CZp4EdWpZNsPqjOQmla4ejMN2zTl2JZNCGVn0WHA6eTPefewPnt3fM7x0ZFvo3Z51MrN5sDne9ny9hp+2KEVtWrnYFkhWvY8iV2btqfiZYiwfMUq2rVrQ+vWLcnOzmbYsEFMmz77iH4nntiWhg2OZcnSFaVtW7ft4JyzTycrK4tatWpxztm9Mq9kUYUlaOLNsphrZncDd3jM30Rm9mvgrRqNrJp5uJi5455j6KRbsKwQa6e8zeebtnPWL4fwyZot5M99l/l3/YWL7r2ablf1xR1m3vRHAP6x9wDL//wml0+bgLuzef5qNr+1KsWvKD2NvfNelr+3hj179nL+4Mv4+VU/Y0glJ6TkSOFwmF/ceAczZ/yVrFCIZ5+bzPr1HzL+zptZsXI106fPAWDE8EFMefmNw/Z99dXp/OTcM1n13jzcndmzFjB9xpxUvIwaE8RSRKKsotoTgJn9APgvoDtQkoF+DKwArnb3/fEOcN/xlwXxiyijjFk5IdUhZLw6eWenOoSjQtE32793Nl3c9JKEc86Zn7wSqOxd6QjZ3b8CRpjZCUDJWax17r65xiMTEfkO0vim03Gnva0HXgAmu/u0yvqKiASBlzOjKl3EO6k3EqgPzDazZWZ2o5nlxdlHRCRlitwSXuIxs75m9oGZ5ZvZreVsb2Vm883sPTNbY2YXR9uzzew5M1trZhvM7LZEYq80Ibv7ane/zd3bAr8AjgeWmtlbZvZviRxARCSZHEt4qYyZZQGPA/2AjsBIM+tYptsdwBR37wKMAJ6Itg8Fct39FKArcI2ZtY4Xe8IT8dx9qbuPAS4HGgKPJbqviEiyFFdhiaMHkO/um939G+AlYFCZPg4cE318LLAjpv0HZlaLyPThb4C98Q6Y0MWFoj+hHgkMAT4GJgIvJ7KviEgyVWMNuTmwLWa9ACj7s8bxREq6NxD5bcYF0fZXiCTvQqAuMMbddxNHvJN6vwWGAXuIfDuc6e4FcV+GiEiKVGWWhZmNBkbHNE1094klm8vZpeyUupHAs+7+oJn1Ap43s05ERtdhII9IRWGRmc2NN0Mt3gi5M/Cv7r4wGvzlZjYE+DswPpGMLyKSTOEqjJCjyXdiBZsLgJYx6y34tiRR4iqgb/S5lphZbeA44FLgv939EPCZmS0GugGVJuR4NeSmwPsAZnYOcC8wCfiykhchIpIyxZb4EsdyoL2ZtTGzHCIn7aaW6bMVOB/AzDoAtYGd0fbzLOIHwOnAxngHjDdCDsWMgocTGc6/CrxqZvrtsIgETnE11ZDdvcjMrgdmAVnA0+6+zswmACvcfSpwE/AnMxtDpJwxyt3dzB4HniEyoDXgGXdfE++Y8RJyLTOr5e5FRL4FYmstR83dRkQkfVTntRrcfSYws0zbuJjH64Ezy9lvP5Gpb1USL6m+CLxtZruAg8AiADNrR6RsISISKBn702l3v9vM5gHNgNkxV3wLATfUdHAiIlVVXM4F/NNF3LKDuy8tp+3DmglHROT7Cac6gO9BdWARySgJzJ4ILCVkEcko1TXLIhWUkEUko6TzHTGUkEUko6hkISISEBk77U1EJN2ENUIWEQkGjZBFRAJCCVlEJCASuFVeYCkhi0hG0QhZRCQg9NNpEZGA0DxkEZGAUMlCRCQglJBFRAJC17IQEQkI1ZBFRAJCsywqMe7ThTV9iKPe7XlnpzqEjHdwx6JUhyAJKk7jooVGyCKSUXRST0QkINJ3fKyELCIZRiNkEZGAKLL0HSMrIYtIRknfdKyELCIZRiULEZGA0LQ3EZGASN90rIQsIhlGJQsRkYAIp/EYOZTqAEREqlNxFZZ4zKyvmX1gZvlmdms521uZ2Xwze8/M1pjZxeVs329mNycSuxKyiGQUr8J/lTGzLOBxoB/QERhpZh3LdLsDmOLuXYARwBNltj8MvJlo7CpZiEhGqcYacg8g3903A5jZS8AgYH1MHweOiT4+FthRssHMBgObga8SPaBGyCKSUYrxhBczG21mK2KW0TFP1RzYFrNeEG2LNR64zMwKgJnADQBm9gPgV8CvqxK7RsgiklGqckrP3ScCEyvYXN6l7ss+/UjgWXd/0Mx6Ac+bWSciifhhd99vlvgV85WQRSSjFFXfLIsCoGXMegtiShJRVwF9Adx9iZnVBo4DegKXmNl9QAOg2My+dvfHKjugErKIZJR4J+uqYDnQ3szaANuJnLS7tEyfrcD5wLNm1gGoDex099K7RpjZeGB/vGQMSsgikmGq66SeuxeZ2fXALCALeNrd15nZBGCFu08FbgL+ZGZjiJQzRrn7d/5GUEIWkYxSjSNk3H0mkZN1sW3jYh6vB86M8xzjEz2eErKIZBT9dFpEJCDC371ikHJKyCKSUXT5TRGRgKjOGnKyKSGLSEZRDVlEJCBUshARCQiVLEREAkKzLEREAkIlCxGRgNBJPRGRgFANWUQkINK5ZHFU3THkwgt7s2bNfNatW8jNN//8iO333TeOZcveZNmyN1m7dgGffLK2dNtdd93GypVzWLlyDpdcMiCZYaedi/qcy7r3F7Jx/f9wy9jrjtj+4P3jWbF8NiuWz2b9ukXs+uzbO+Lce89/sHrVW6xds4CHH5qQzLAzxh2/fYhz+o9g8GX/P9WhpIS7J7wEzVEzQg6FQjz66F307/9TCgoKWbx4GtOnz2Hjxk2lfW655dsEcO21o+jc+WQA+vY9jy5dOtGjR19yc3OYM+dlZs2az759+5P+OoIuFArx+0fvpu/FIykoKGTpkplMmz6bDRu+fZ9vGju+9PF1P7+Szp07AdDr9G6c0as7XU67AICFC16n9zm9eHvhkqS+hnQ3+OILuXTIQG7/zQOpDiUlwhohB1/37p356KOP2bJlK4cOHeLll6cxYECfCvsPGzaQKVOmAtChQ3sWLVpKOBzmwIGDrF27nj59zk1S5OmlR/cuh73PU6a8wcABF1XYf8TwwUye/DoQGdnk1s4lJyeH3NwcamXX4tPPdiYr9IzRrfMpHHtM/VSHkTJVuade0Bw1CTkvrykFBd/efWX79kLy8n5Ubt9WrZrTunUr5s9fDMDateu56KKfUKdObRo3bkjv3mfQokWzpMSdbvKaN2VbzPtcsL2QvLym5faNvM8teSv6Pi9dtpK3F/wvBVvfpWDre8yZ8zYbN+YnJW7JHBlbsojeD2qzuz9Vpn0M0NTdf1WTwVWn8m40WNE/yNChA3nttRkUF0cm0Mydu4iuXX/MggWvsWvXbpYtW0lRUbhG401XVXmfhw8bxKt/+/Z9btu2NSed1J7j23QDYNabL3H2WT1Z9D/Lai5gyThBHPkmKt4I+Z8o/46sjwL9K9op9tba4XAw6qzbtxfSokVe6Xrz5s0oLPys3L5Dhw4oLVeU+N3vHqNnz3707/9TzIz8/C01Gm+62l5QSMuY97lF82YUFn5abt9hwwYxefIbpeuDB/Vl2Tvv8tVXB/jqqwP896y36NnztBqPWTKLV+G/oImXkN3dj5hnHW2r8N7W7j7R3bu5e7esrHrfN8ZqsWLFatq1a0Pr1i3Jzs5m6NABTJ8+54h+7dufQMOGx7J06crStlAoRKNGDQDo1OkkOnXqwNy5C5MWezpZvmLVYe/zsGGDmDZ99hH9TjyxLQ0bHMuSpStK27Zu28E5Z59OVlYWtWrV4pyze6lkIVUWdk94CZp4sywOmFl7d98U22hm7YGDNRdW9QuHw9x4438ybdrzZGVl8dxzk9mw4UPGjfslK1euZcaMSHIePnwQU6ZMO2zf7Oxs5s17FYC9e/dx5ZW/IBxWyaI84XCYX9x4BzNn/JWsUIhnn5vM+vUfMv7Om1mxcnXpl+CI4YOY8vIbh+376qvT+cm5Z7LqvXm4O7NnLWD6jCO/NKVyY++8l+XvrWHPnr2cP/gyfn7VzxhSyYnVTJPOJQurrLBtZv2APwB3ASVDxm7AbcCN0RsAVqp27Vbp++6kiaJifTnUtIM7FqU6hKNC9nEnVPiXd6J6Nf9Jwjlnyfb53/t41anSEbK7v2lmg4GxwA3R5nXAEHdfW/GeIiKpEcTZE4mK+8MQd38fuCIJsYiIfG/pXLKo9KSemR1nZnea2b+bWT0ze9LM3jezN8ysXbKCFBFJVCbPsvgrkAu0B94BtgCXANOBP9dsaCIiVRf24oSXoIlXsviRu99ukdn+f3f3+6LtG83syKvGiIikWCbXkMMQmYxsZrvKbAve14uIHPXSuYYcLyGfYGZTifwIpOQx0fU2NRqZiMh3EMTacKLiJeRBMY9LruXnZdZFRAKjOINLFg2AFu7+OICZvQM0IZKU0+bCQiJy9MjkEfItwIiY9Rwiv9T7AfAM8HINxSUi8p0EcfZEouJNe8tx920x6//j7p+7+1YiSVlEJFCK3RNe4jGzvmb2gZnlm9mt5WxvZWbzzew9M1tjZhfHbLstut8HZpbQxUTijZAbxq64+/Uxq00SOYCISDJVV8nCzLKAx4ELgQJguZlNdff1Md3uAKa4+5Nm1hGYCbSOPh4BnAzkAXPN7ER3r/TCM/FGyMvM7N/KCfQaIj8UEREJlGocIfcA8t19s7t/A7zE4RMdIHI+7Zjo42OBktvlDAJecvd/uPsWID/6fJWKN0IeA7xuZpcC70bbuhL59d7geE8uIpJsVRkhm9loYHRM00R3L7kpR3MgtmRbAPQs8xTjgdlmdgORMu4FMfsuLbNv83jxxLva22fAGWZ2HpGhN8AMd38r3hOLiKRCuPKqwGGiybe8uyJB+TfhKJvtRwLPuvuDZtYLeN7MOiW47xHiXu0NIJqAlYRFJPCq8afTBUDLmPUWfFuSKHEV0Dd63CVmVhs4LsF9j3DU3HVaRI4OxXjCSxzLgfZm1sbMcoicpJtaps9W4HwAM+sA1AZ2RvuNMLNcM2vDtxdoq1RCI2QRkXRRXSNkdy8ys+uBWUAW8LS7rzOzCcAKd58K3AT8yczGEClJjPJIAOvMbAqwHigCros3wwLi3MKpOugWTjVPt3CqebqFU3JUxy2cmjXomHDOKdyzPn1u4SQikm4y+afTIiJpJZ1/Oq2ELCIZJZMvUC8iklYy+fKbIiJpRSNkEZGAyORbOImIpBWNkEVEAkKzLEREAkIn9UREAkIlCxGRgNAv9UREAkIjZBGRgEjnGnKNX+0tHZnZ6JjbuEgN0Htc8/Qepx9doL58o+N3ke9J73HN03ucZpSQRUQCQglZRCQglJDLp7pbzdN7XPP0HqcZndQTEQkIjZBFRAJCCVlEJCCOuoRsZmEzW2Vm75vZNDNrEG1vbWYHo9tKlpzotn7S9L5HAAADBElEQVRmtsLMNpjZRjN7ILWvIvjM7D/MbJ2ZrYm+l2+a2T1l+nQ2sw3Rx/XM7I9m9lF0v4Vm1jM10Qefmf3IzP5qZpvNbKWZLTGzfzazc83sy+h7vsbM5prZD2P202c5wI66hAwcdPfO7t4J2A1cF7Pto+i2kuUbM+sEPAZc5u4dgE7A5hTEnTbMrBfwT8Bp7n4qcAFwLzC8TNcRwF+jj/9M5N+jvbufDIwCjktKwGnGzAx4HVjo7ie4e1ci72WLaJdF0c/vqcByop9xfZaD72hMyLGWAM3j9LkFuNvdNwK4e5G7P1HjkaW3ZsAud/8HgLvvcve3gT1lRr3DgJfMrC3QE7jDPXIxW3ff7O4zkh14mjgP+MbdnyppcPe/u/sfYjtFE3d94Itokz7LAXfUJmQzywLOB6bGNLeNKVc8Hm3rBKxMeoDpbTbQ0sw+NLMnzKx3tP1FIiM5zOx04HN33wScDKxy93Bqwk07JwPvVrL9bDNbBWwl8tfJ09F2fZYD7mhMyHWiH9bPgUbAnJhtsSWL68rfXeJx9/1AVyI/3d0JTDazUcBLwCVmFiKSmF9MWZAZxMweN7PVZrY82lRSsmgJPAPcl8LwpAqOxoR80N07A8cDORxeQy7POiLJRarA3cPuvsDd7wSuB4a4+zbgY6A3MASYEu2+DvhxNFFLfOuA00pWooOH84Em5fSdCpwTs58+ywF21P4P4O5fAv8O3Gxm2ZV0vR+43cxOBDCzkJn9Mhkxpisz+39m1j6mqTPw9+jjF4GHifw1UgDg7h8BK4BfR+uemFl7MxuUxLDTyVtAbTO7NqatbgV9zwI+ij7WZzngjurrIbv7e2a2msifz4sq6LPGzG4EXjSzuoADOtlUuXrAH6JTCouAfL698tjLwKPADWX2uRp4EMg3swNESkpjkxNuenF3N7PBwMNmdguRstBXwK+iXUpqyAZ8SeS91Wc5Dein0yIiAXHUlixERIJGCVlEJCCUkEVEAkIJWUQkIJSQRUQCQglZRCQglJBFRALi/wAJOMD8L+iCkgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_Survived_RFC = pd.Series(RFC_best.predict(test), name='RFC')\n",
    "test_Survived_SVMC = pd.Series(SVMC_best.predict(test), name='SVC')\n",
    "test_Survived_GBC = pd.Series(GBC_best.predict(test), name='GBC')\n",
    "\n",
    "ensemble_results = pd.concat([test_Survived_RFC, test_Survived_SVMC, test_Survived_GBC], axis=1)\n",
    "g = sns.heatmap(ensemble_results.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.2 Ensemble modeling "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2.1 combining models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "votingC = VotingClassifier(estimators=[('rfc', RFC_best),\n",
    "                                       ('svc', SVMC_best),\n",
    "                                       ('gbc', GBC_best)],\n",
    "                                       voting='soft', n_jobs=-1)\n",
    "votingC = votingC.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# votingC = GBC_best.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.3 Prediction "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    }
   ],
   "source": [
    "test_Survived = pd.Series(votingC.predict(test), name='Survived')\n",
    "results = pd.concat([IDtest, test_Survived], axis=1)\n",
    "results.to_csv('ensemble_ptyhon_voting_2019_11_11.csv', index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
